import tensorflow as tf

img = tf.keras.utils.get_file('zebra.jpg','https://i.imgur.com/XjeiRMV.jpg')

import cv2
import matplotlib.pyplot as plt

im = cv2.imread(img)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)

im_ = im.copy()

rec1 = cv2.rectangle(im_, (120,25),(200,165), color=(255,0,0), thickness=2)
rec2 = cv2.rectangle(im_, (300,50),(480,320), color=(255,0,0), thickness=2)

plt.imshow(im_)

im_.shape
# (333, 500, 3)

h = w = 800
im_r = cv2.resize(im, (h,w))
im_r_ = im_r.copy()

import numpy as np
x = np.array([120, 25, 200, 165])
y = np.array([300, 50, 480,320])
x[0] = int(x[0]*(w/im.shape[1]))
x[1] = int(x[1]*(h/im.shape[0]))
x[2] = int(x[2]*(w/im.shape[1]))
x[3] = int(x[3]*(h/im.shape[0]))
y[0] = int(y[0]*(w/im.shape[1]))
y[1] = int(y[1]*(h/im.shape[0]))
y[2] = int(y[2]*(w/im.shape[1]))
y[3] = int(y[3]*(h/im.shape[0]))
rec1 = cv2.rectangle(im_r_, (x[0],x[1]),(x[2],x[3]), color=(255,0,0), thickness=2)
rec2 = cv2.rectangle(im_r_, (y[0],y[1]),(y[2],y[3]), color=(255,0,0), thickness=2)

from skimage.util import view_as_blocks, view_as_windows

plt.figure(figsize=(8,8))
plt.imshow(im_r_)

vgg = tf.keras.applications.VGG16(include_top=False)

for j,i in enumerate(vgg.layers):
    output = tf.keras.models.Model(vgg.input, i.output)
    print(output(im_r_[tf.newaxis]).shape,j)
    
(1, 800, 800, 3) 0
(1, 800, 800, 64) 1
(1, 800, 800, 64) 2
(1, 400, 400, 64) 3
(1, 400, 400, 128) 4
(1, 400, 400, 128) 5
(1, 200, 200, 128) 6
(1, 200, 200, 256) 7
(1, 200, 200, 256) 8
(1, 200, 200, 256) 9
(1, 100, 100, 256) 10
(1, 100, 100, 512) 11
(1, 100, 100, 512) 12
(1, 100, 100, 512) 13
(1, 50, 50, 512) 14
(1, 50, 50, 512) 15
(1, 50, 50, 512) 16
(1, 50, 50, 512) 17
(1, 25, 25, 512) 18

backbone = tf.keras.models.Model(vgg.input, vgg.layers[17].output)

backbone(im_r_[tf.newaxis]).shape
# TensorShape([1, 50, 50, 512])

plt.imshow(backbone(im_r_[tf.newaxis])[0,...,4])

vgg.summary()
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, None, None, 3)]   0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

vgg(im_r_[tf.newaxis])


<tf.Tensor: shape=(1, 25, 25, 512), dtype=float32, numpy=
array([[[[ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.142358  , ...,  0.        ,
           0.        ,  0.        ],
         ...,
         [ 0.        ,  0.        ,  1.3040222 , ...,  0.        ,
           2.3414693 ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           3.648667  ,  0.        ],
         [ 0.        ,  0.        ,  2.5827253 , ...,  0.        ,
           1.2787921 ,  0.        ]],

        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         ...,
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ]],

        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         ...,
         [ 0.        ,  0.        , 25.991032  , ...,  0.        ,
           4.2155175 ,  0.        ],
         [ 0.        ,  0.        ,  9.656704  , ...,  0.        ,
           6.1238546 ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ]],

        ...,

        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
           2.6076157 ,  0.24721637],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  1.4595927 ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.13756028],
         ...,
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ]],

        [[15.054876  ,  0.        ,  0.        , ...,  0.        ,
           2.182668  ,  0.        ],
         [11.117934  ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         ...,
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ]],

        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.29810184,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           1.1234534 ,  0.        ],
         ...,
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ],
         [ 0.        ,  0.        ,  0.        , ...,  0.        ,
           0.        ,  0.        ]]]], dtype=float32)>

im_r = cv2.resize(im, (h,w))
im_r_ = im_r.copy()

x = np.arange(8,800,16)
y = np.arange(8,800,16)

cl = np.array(np.meshgrid(x,y)).T.reshape(-1,2)

for i in range(2500):
    cv2.circle(im_r_, (cl[i,0], cl[i,1]),1, (255,0,0), thickness=2)
    
plt.figure(figsize=(10,10))
plt.imshow(im_r_)

50*50*9
# 22500

ratio = [0.5, 1, 2]
scale = [8,16,32]

al = np.zeros((22500,4))
count = 0
for i in cl:
    cx, cy = i[0],i[1]
    for r in ratio:
        for s in scale:
            h = pow(pow(s,2)/r,0.5)
            w = h*r
            h *= 16
            w *= 16
            xmin = cx-0.5*w
            ymin = cy-0.5*h
            xmax = cx+0.5*w
            ymax = cy+0.5*h
            al[count] = [xmin, ymin,xmax,ymax]
            count += 1

al.shape
# (22500, 4)

point = 570

im_r_
array([[[ 87,  51,  37],
        [ 91,  52,  40],
        [ 97,  54,  45],
        ...,
        [ 75,  48,  37],
        [ 67,  45,  35],
        [ 61,  43,  33]],

       [[ 87,  51,  37],
        [ 91,  53,  40],
        [ 97,  54,  45],
        ...,
        [ 74,  47,  36],
        [ 66,  44,  34],
        [ 60,  42,  32]],

       [[ 86,  52,  38],
        [ 90,  53,  41],
        [ 95,  54,  44],
        ...,
        [ 70,  43,  32],
        [ 62,  40,  30],
        [ 56,  38,  29]],

       ...,

       [[153,  93,  65],
        [106,  63,  43],
        [ 49,  28,  16],
        ...,
        [ 85,  50,  28],
        [129,  84,  55],
        [166, 113,  78]],

       [[106,  59,  47],
        [ 95,  50,  37],
        [ 82,  41,  25],
        ...,
        [108,  75,  51],
        [115,  75,  51],
        [123,  78,  53]],

       [[ 92,  49,  42],
        [ 91,  46,  35],
        [ 91,  45,  28],
        ...,
        [114,  83,  58],
        [110,  73,  50],
        [110,  68,  46]]], dtype=uint8)

# img_ = np.copy(im_r)
# for i in range(point,point+9):
#     x_min = int(al[i][0])
#     y_min = int(al[i][1])
#     x_max = int(al[i][2])
#     y_max = int(al[i][3])
#     cv2.rectangle(img_, (x_min,y_min),(x_max,y_max), (0,255,0), thickness=4)
# for i in range(2500):
#     cv2.circle(img_, (cl[i,0], cl[i,1]),1, (0,0,255), thickness=2)    

x = np.array([120, 25, 200, 165])
y = np.array([300, 50, 480,320])

x[0] = int(x[0]*1.6)
x[1] = int(x[1]*2.4)
x[2] = int(x[2]*1.6)
x[3] = int(x[3]*2.4)
y[0] = int(y[0]*1.6)
y[1] = int(y[1]*2.4)
y[2] = int(y[2]*1.6)
y[3] = int(y[3]*2.4)

rec1 = cv2.rectangle(im_r_, (x[0],x[1]),(x[2],x[3]), color=(255,0,0), thickness=5)
rec2 = cv2.rectangle(im_r_, (y[0],y[1]),(y[2],y[3]), color=(255,0,0), thickness=5)    

plt.imshow(im_r_)

22500 > 0< 800> 제외

np.where((al[:,0] >=0) & (al[:,1] >=0) &  (al[:,2] <= 800 ) &  (al[:,3] <= 800 ))
# (array([ 1404,  1413,  1422, ..., 21069, 21078, 21087], dtype=int64),)

is_al = al[np.where((al[:,0] >=0) & (al[:,1] >=0) &  (al[:,2] <= 800 ) &  (al[:,3] <= 800 ))]

len(is_al) # anchor 
# 8940

def iou(box1,box2):
    x1 = max(box1[0], box2[0])
    y1 = max(box1[1], box2[1])
    
    x2 = min(box1[2], box2[2])
    y2 = min(box1[3], box2[3])
    
    if (x1 < x2 and  y1 < y2):
        w_o = x2 - x1
        h_o = y2 - y1
        area = w_o*h_o
    else:
        return 0
    
    area_b1 = (box1[2]-box1[0])*(box1[3]-box1[1])
    area_b2 = (box2[2]-box2[0])*(box2[3]-box2[1])
    union = area_b1 + area_b2 - area
    
    return area/union

object 1 = x

x = np.array([300, 50, 480,320])
x[0] = int(x[0]*1.6)
x[1] = int(x[1]*2.4)
x[2] = int(x[2]*1.6)
x[3] = int(x[3]*2.4)

object 2 = y

y = np.array([120, 25, 200, 165])
y[0] = int(y[0]*1.6)
y[1] = int(y[1]*2.4)
y[2] = int(y[2]*1.6)
y[3] = int(y[3]*2.4)


objects = [x,y]

result = np.zeros((8940,len(objects)))

for t,g in enumerate(objects):
    for i,j in enumerate(is_al):
        result[i][t] = iou(j,g)
        

result
array([[0.        , 0.        ],
       [0.        , 0.        ],
       [0.        , 0.        ],
       ...,
       [0.06581804, 0.        ],
       [0.06484636, 0.        ],
       [0.05869298, 0.        ]])

anchor_id = np.where((al[:,0] >=0) & (al[:,1] >=0) &  (al[:,2] <= 800 ) &  (al[:,3] <= 800 ))
anchor_id[0]
# array([ 1404,  1413,  1422, ..., 21069, 21078, 21087], dtype=int64)

pandas : 2차원

data = pd.DataFrame(data=[anchor_id[0], result[:,0], result[:,1]]).T

data.rename(columns={0:'anchor_id', 1:'o1_iou',2:'o2_iou'}, inplace=True)

data.anchor_id = data.anchor_id.astype('int')
data
	anchor_id	o1_iou	o2_iou
0	1404	0.000000	0.0
1	1413	0.000000	0.0
2	1422	0.000000	0.0
3	1431	0.000000	0.0
4	1440	0.000000	0.0
...	...	...	...
8935	21051	0.065818	0.0
8936	21060	0.065818	0.0
8937	21069	0.065818	0.0
8938	21078	0.064846	0.0
8939	21087	0.058693	0.0

data['o1_iou_objectness'] = data.apply(lambda x: 1 if x['o1_iou'] > 0.7 else -1, axis=1)

data[data['o1_iou_objectness'] == 1]
	anchor_id	o1_iou	o2_iou	o1_iou_objectness
7540	16877	0.711914	0.0	1
7547	16886	0.711914	0.0	1
7768	17327	0.711914	0.0	1
7775	17336	0.711914	0.0	1

data.o2_iou.argmax()
# 1785


data.loc[data.o2_iou.argmax()]
anchor_id            6418.00000
o1_iou                  0.00000
o2_iou                  0.65625
o1_iou_objectness      -1.00000
Name: 1785, dtype: float64

top
array([[418.98066402,  45.96132803, 781.01933598, 770.03867197],
       [418.98066402,  61.96132803, 781.01933598, 786.03867197],
       [434.98066402,  45.96132803, 797.01933598, 770.03867197],
       [434.98066402,  61.96132803, 797.01933598, 786.03867197]])

img_ = np.copy(im_r)

for i,j in enumerate(top):
    x_min = int(top[i][0])
    y_min = int(top[i][1])
    x_max = int(top[i][2])
    y_max = int(top[i][3])
    cv2.rectangle(img_, (x_min,y_min),(x_max,y_max), (0,255,0), thickness=1)

# x = np.array([120, 25, 200, 165])
# y = np.array([300, 50, 480,320])

# x[0] = int(x[0]*1.6)
# x[1] = int(x[1]*2.4)
# x[2] = int(x[2]*1.6)
# x[3] = int(x[3]*2.4)
# y[0] = int(y[0]*1.6)
# y[1] = int(y[1]*2.4)
# y[2] = int(y[2]*1.6)
# y[3] = int(y[3]*2.4)

# # rec1 = cv2.rectangle(im_r_, (x[0],x[1]),(x[2],x[3]), color=(255,0,0), thickness=5)
# # rec2 = cv2.rectangle(im_r_, (y[0],y[1]),(y[2],y[3]), color=(255,0,0), thickness=5)    
plt.figure(figsize=(10,10))
plt.imshow(img_)

Faster R-CNN

Fast R-CNN에서 Selective search를 제거하고 대신에 Region proposal network를 통해 대략적인 물체의 위치를 찾는 방식을 사용한다

처음 부터 끝까지 CNN안에서 작동하기 때문에 Fast R-CNN보다 훨씬 더 빠르다

Region Proposal

물체가 있을 만한 위치를 찾는 방법

1. Selective search

- 색상, 질감, 크기 등을 기준으로 유사도를 비교하여 영역들을 통합한다

이때 threshold에 따라서 후보 영역들을 만들어 낸다

(selective search 논문에서는 2000개의 후보영역을 만들어냈다)

2. Edge boxes

- Gradient magnitude와 gradient orientation을 사용하여 edge group을 표현하고,

이를 이용하여 bounding box score를 찾아내는 방법

3. Region proposal network

Region proposal network

원본 이미지에서 region proposals를 추출하는 네트워크
RPN은 각 위치에서 객체의 경계와 IOU점수를 동시에 예측하는 fully connected convolution network이다

import tensorflow as tf 
import cv2
import matplotlib.pyplot as plt
import numpy as np

img = tf.keras.utils.get_file('zebra.jpg', 'https://i.imgur.com/XjeiRMV.jpg')

im = cv2.imread(img)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
plt.imshow(im)

im_ = im.copy()

rec1 = cv2.rectangle(im_, (120,25),(200,165), color=(255,0,0), thickness=2)
rec2 = cv2.rectangle(im_, (300,50),(480,320), color=(255,0,0), thickness=2)

plt.imshow(im_)

h = w = 800

plt.imshow(cv2.resize(im, (w,h), interpolation=cv2.INTER_NEAREST))

im_.shape
# (333, 500, 3)

800/333, 800/500
# (2.4024024024024024, 1.6)

im_r = cv2.resize(im, (h,w))
im_r_ = im_r.copy()

x = np.array([120,25,200,165])
y = np.array([300,50,480,320])

# mapping 
x[0] = int(x[0]*(w/im.shape[1])) # 비율 곱 
x[1] = int(x[1]*(h/im.shape[0]))
x[2] = int(x[2]*(w/im.shape[1]))
x[3] = int(x[3]*(h/im.shape[0]))

y[0] = int(y[0]*(w/im.shape[1]))
y[1] = int(y[1]*(h/im.shape[0]))
y[2] = int(y[2]*(w/im.shape[1]))
y[3] = int(y[3]*(h/im.shape[0]))

rec1 = cv2.rectangle(im_r_, (x[0],x[1]),(x[2],x[3]), color=(255,0,0), thickness=2)
rec2 = cv2.rectangle(im_r_, (y[0],y[1]),(y[2],y[3]), color=(255,0,0), thickness=2)

plt.imshow(im_r_)

vgg = tf.keras.applications.VGG16(include_top=False)

vgg.summary()
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, None, None, 3)]   0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

# 800x800 이미지가 최종적으로 25x25로 줄어든다 (convolutional layer를 통과하기 때문에)
for i in vgg.layers:
  output = tf.keras.models.Model(vgg.input, i.output)
  print(output(im_r_[tf.newaxis]).shape)
  
(1, 800, 800, 3)
(1, 800, 800, 64)
(1, 800, 800, 64)
(1, 400, 400, 64)
(1, 400, 400, 128)
(1, 400, 400, 128)
(1, 200, 200, 128)
(1, 200, 200, 256)
(1, 200, 200, 256)
(1, 200, 200, 256)
(1, 100, 100, 256)
(1, 100, 100, 512)
(1, 100, 100, 512)
(1, 100, 100, 512)
(1, 50, 50, 512)
(1, 50, 50, 512)
(1, 50, 50, 512)
(1, 50, 50, 512)
(1, 25, 25, 512)

# 몇번째 layer까지 사용하는지에 따라 mapping하는 이미지 비율이 달라진다
backbone = tf.keras.models.Model(vgg.input, vgg.layers[17].output) # Faster R-CNN 논문에서는 17번째 layer까지 사용했다

backbone(im_r_[tf.newaxis]).shape
# TensorShape([1, 50, 50, 512])

from skimage.util import view_as_blocks, view_as_windows 겹쳐서 자르기, 겹치지 않고 자르기  

# 800x800 => 50x50  :   16x16이미지가 1x1로 표현된다는 의미이다    

x = np.arange(8,800,16)
y = np.arange(8,800,16)
cl = np.array(np.meshgrid(x,y)).T.reshape(-1,2) # 중점들 모음 array 

cl.shape
# (2500, 2)

im_r = cv2.resize(im, (h,w))
im_r_ = im_r.copy()

for i in range(2500):
  cv2.circle(im_r_, (cl[i,0],cl[i,1]), 1, (255,0,0), thickness=2)
  

plt.figure(figsize=(10,10))
plt.imshow(im_r_) # 50x50 영역을 표기하기 위한 점들

ratio = [0.5,1,2]
scale = [8,16,32]
al = np.zeros((22500,4)) # 50x50x9개 그림을 그릴 수 있다 
count = 0

같은 색 영역들은 모양만 다르고 가능한 영역의 크기들은 근접하게 만들어야 한다

(위 그림의 예시는 800x600 이미지에 대한 anchor boxes를 만든 것이다)

800x800 이미지에 대한 anchor box의 중점은 800/16 * 800/16 = 2500 이고,

중점당 anchor box의 개수가 9개 이므로 2500*9 = 22500 총 22500개의 anchor boxes가 생성이 된다

for i in cl:
  cx, cy = i[0], i[1]
  for r in ratio:
    for s in scale:
      h = pow(pow(s,2)/r,0.5) # 지수를 사용하고 루트를 씌우는 이유 : numerical stability때문에 
      w = h*r
      h *= 16 # 1칸에 16이기 때문에?
      w *= 16 
      xmin = cx-0.5*w
      ymin = cy-0.5*h
      xmax = cx+0.5*w
      ymax = cy+0.5*h
      al[count] = [xmin,ymin,xmax,ymax]
      count += 1

al
# array([[ -37.254834  ,  -82.50966799,   53.254834  ,   98.50966799],
       [ -82.50966799, -173.01933598,   98.50966799,  189.01933598],
       [-173.01933598, -354.03867197,  189.01933598,  370.03867197],
       ...,
       [ 701.49033201,  746.745166  ,  882.50966799,  837.254834  ],
       [ 610.98066402,  701.49033201,  973.01933598,  882.50966799],
       [ 429.96132803,  610.98066402, 1154.03867197,  973.01933598]])

img_ = np.copy(im_r)

point = 11465
for i in range(point,point+9):
  x_min = int(al[i][0])
  y_min = int(al[i][1])
  x_max = int(al[i][2])
  y_max = int(al[i][3])
  cv2.rectangle(img_, (x_min,y_min),(x_max,y_max),(0,255,0),thickness=4)

for i in range(2500):
  cv2.circle(img_, (cl[i,0],cl[i,1]), 1, (255,0,0), thickness=2)

x = np.array([120,25,200,165])
y = np.array([300,50,480,320])

x[0] = int(x[0]*(w/im.shape[1])) 
x[1] = int(x[1]*(h/im.shape[0]))
x[2] = int(x[2]*(w/im.shape[1]))
x[3] = int(x[3]*(h/im.shape[0]))

y[0] = int(y[0]*(w/im.shape[1]))
y[1] = int(y[1]*(h/im.shape[0]))
y[2] = int(y[2]*(w/im.shape[1]))
y[3] = int(y[3]*(h/im.shape[0]))

rec1 = cv2.rectangle(img_, (x[0],x[1]),(x[2],x[3]), color=(255,0,0), thickness=3)
rec2 = cv2.rectangle(img_, (y[0],y[1]),(y[2],y[3]), color=(255,0,0), thickness=3)

plt.imshow(img_)

물체를 인식하는 사각형 하나와 22500개의 anchor box가 겹치는 부분을 구한다

그렇게 해서 계산된 IOU값을 구하고 가장 많이 겹치는 anchor box를 구한다

논문에서는 IOU가 0.7보다 높은 것을 사용하고 0.3 보다 작은 것은 없는 것으로 판단한다

Fast R-CNN

Fast R-CNN의 구조 Feature Extractor -> ROI Pooling -> Classifier & Regressor ※ ROI(Region of interest): 관심 영역

Feature extractor

R-CNN이나 SPPNet에서 사용된것과 같이 CNN을 통해 이미지의 Feature Map을 추출하는 단계이다

R-CNN을 떠올려보면 먼저 Selective Search를 이용해 이미지 하나 당 약 2000개의 RoI를 뽑아내고 이를 모두 CNN에 통과시켰기 때문에 엄청난 처리시간이 요구되었다

또한 추출된 RoI는 서로 겹치는 부분이 굉장히 많이 발생하기 때문에 같은 영역의 이미지가 CNN에 여러번 들어가게 되므로 비효율적이다

반면, Fast R-CNN은 Selective Search를 적용할 때 이미지를 잘라내는 것이 아니라 그 좌표와 크기정보만을 (r, c, h, w) 추출해 낸다

이는 이미지에 비해 굉장히 적은 용량이기 때문에 다른 저장공간을 요구하지 않는다는 장점이 있다

그리고 CNN에는 이미지 한 장 만이 들어가 공통적인 Feature Map을 추출하고, 각 RoI들은 모델을 통과하며 줄어든 크기의 비율을 따져 좌표만 변경시킨다.

이를 RoI Projection이라고 한다

ROI pooling

이미지 하나의 feature map + RoI들의좌표들을 동일한 크기로 변환하는 layer를 거친다

이를 RoI pooling layer라고 하고 한 층짜리 spatial pyramid pooling layer와 똑같다고 생각 하면 된다.

spatial pyramid pooling할 때 층층이 쌓인 피라미드가 누적되서 더 좋은 효과를 내지 못한다는 사실을 알게 되었기 때문에 굳이 하지 않게 되었다

Classifier & Regressor

Classifier는 물체가 무엇인지 구별하는 classification 역할을 하고

Regressor는 물체의 영역을 표시하기 위한 localization 역할을 한다

R-CNN에서는 이 둘을 따로 학습시켰지만, Fast R-CNN에서는 Multi-task loss function을 고안해서 End-to-end로 학습이 가능해 졌다

보통 기존 Loss에 새로운 Loss를 추가할 때에는 위 식처럼 덧셈으로 연결한 뒤, 새로운 Loss의 영향력을 조절하기 위해 가중치 λ를 붙여준다

Lcls는 따로 SVM을 학습시키지 않고 Classification을 하기 위한 Softmax 함수이며, Lloc는 Localization을 위한 L1 Loss 함수이다

λ는 1로 고정시켰고, [u≥1]은 'Classification 결과가 Background (u=0)이면 Lloc를 죽이고, 그렇지 않으면 살린다'는 의미이다

Bounding Box를 치고자 하는 대상은 Background가 아닌 Object이기 때문이다

R-CNN과 SPPNet에서 사용한 L2 대신 L1을 사용한 이유는 덜 민감한 함수이기 때문에 Fine Tuning을 사용하기 쉬워진다

import pandas as pd 
import tensorflow as tf 


air = pd.read_csv('dataset/annotations/airplane.csv', header=None, names=['filename','x1','y1','x2','y2','class'])
face = pd.read_csv('dataset/annotations/face.csv', header=None, names=['filename','x1','y1','x2','y2','class'])
motorcycle = pd.read_csv('dataset/annotations/motorcycle.csv', header=None, names=['filename','x1','y1','x2','y2','class'])
air.filename = air.filename.map(lambda x : 'airplane/'+x)
face.filename = face.filename.map(lambda x : 'face/'+x)
motorcycle.filename = motorcycle.filename.map(lambda x : 'motorcycle/'+x)
data = pd.concat([air, face, motorcycle], ignore_index=True)

data = pd.concat([data, pd.get_dummies(data['class'])], axis=1)
data.drop(columns='class',inplace=True)

data.columns[1:]
# Index(['x1', 'y1', 'x2', 'y2', 'airplane', 'face', 'motorcycle'], dtype='object')

dig = tf.keras.preprocessing.image.ImageDataGenerator() # dataframe을 바로 불러올 수 있기 때문에 imagedatagenerator를 사용한다
dig = dig.flow_from_dataframe(data, 'dataset/images/', class_mode='raw', y_col=data.columns[1:], target_size=(224,224))
# Found 2033 validated image filenames.

def flow(x):
  while True:
    (X,y) = next(x)
    yield X, (y[:,:4],y[:,4:]) # X는 그대로, y는 4개, 3개로 분리 
    
dfg = tf.data.Dataset.from_generator(lambda : flow(dig), output_shapes=(((None,224,224,3)), ((None,4), (None,3))),
                                                  output_types=((tf.float32), (tf.float32, tf.float32)))

next(iter(dfg))[1][0] # y값 (4개)

<tf.Tensor: shape=(32, 4), dtype=float32, numpy=
array([[113.,   9., 286., 239.],
       [ 61.,  47., 210., 141.],
       [ 41.,  22., 225., 134.],
       [ 39.,  32., 226., 149.],
       [ 53.,  29., 349., 135.],
       [ 49.,  28., 346., 116.],
       [ 53.,  63., 335., 138.],
       [ 32.,  16., 230., 113.],
       [121.,  34., 314., 302.],
       [ 32.,  25., 228., 164.],
       [ 37.,  22., 233., 140.],
       [ 34.,  42., 230., 133.],
       [ 35.,  16., 230., 112.],
       [ 75.,  25., 259., 283.],
       [ 46.,  37., 342., 140.],
       [180.,  23., 361., 276.],
       [ 38.,  25., 227., 128.],
       [ 38.,  25., 220., 155.],
       [ 44.,  41., 221., 154.],
       [ 42.,  52., 226., 162.],
       [ 61.,  24., 346., 148.],
       [ 62.,  16., 270., 277.],
       [ 31.,  19., 233., 135.],
       [ 49.,  30., 349., 137.],
       [ 65.,  42., 345., 158.],
       [162.,  47., 414., 302.],
       [ 54.,  30., 345., 130.],
       [ 48.,  31., 350., 112.],
       [ 38.,  24., 224., 127.],
       [ 45.,  24., 237., 142.],
       [ 34.,  19., 230., 127.],
       [ 42.,  36., 231., 150.]], dtype=float32)>

next(iter(dfg))[1][1] # y값 (3개)

<tf.Tensor: shape=(32, 3), dtype=float32, numpy=
array([[0., 0., 1.],
       [0., 0., 1.],
       [1., 0., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [1., 0., 0.],
       [0., 0., 1.],
       [1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.],
       [1., 0., 0.],
       [0., 0., 1.],
       [1., 0., 0.],
       [0., 0., 1.],
       [0., 0., 1.],
       [1., 0., 0.],
       [0., 0., 1.],
       [0., 1., 0.],
       [1., 0., 0.],
       [1., 0., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 0., 1.],
       [1., 0., 0.],
       [0., 0., 1.],
       [0., 1., 0.],
       [0., 1., 0.],
       [1., 0., 0.],
       [0., 0., 1.],
       [0., 0., 1.],
       [1., 0., 0.]], dtype=float32)>

vgg = tf.keras.applications.VGG16(include_top=False, input_shape=(224,224,3))
vgg.trainable = False 

input_ = tf.keras.Input((224,224,3))
preprocess = tf.keras.layers.Lambda(lambda x: tf.keras.applications.vgg16.preprocess_input(x))(input_)
x = vgg(preprocess)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(256, activation='relu')(x)
x = tf.keras.layers.Dense(128, activation='relu')(x)

box = tf.keras.layers.Dense(64, activation='relu')(x)
box = tf.keras.layers.Dense(4, name='box',activation='relu')(box)

target = tf.keras.layers.Dense(64, activation='relu')(x)
target = tf.keras.layers.Dense(3, name='target',activation='softmax')(target)

model=tf.keras.models.Model(input_, [box,target])

loss = {
    'box': tf.keras.losses.MeanAbsoluteError(),
    'target': tf.keras.losses.CategoricalCrossentropy()
}

model.compile(loss=loss)

model.fit(dfg, epochs=20, steps_per_epoch=10) # 한 epoch당 10번 업데이트 
Epoch 1/20
10/10 [==============================] - 199s 16s/step - loss: 90.5833 - box_loss: 80.8374 - target_loss: 9.7459
Epoch 2/20
10/10 [==============================] - 158s 16s/step - loss: 38.9525 - box_loss: 38.9085 - target_loss: 0.0441
Epoch 3/20
10/10 [==============================] - 156s 16s/step - loss: 32.4575 - box_loss: 32.4469 - target_loss: 0.0106
Epoch 4/20
10/10 [==============================] - 155s 16s/step - loss: 28.6964 - box_loss: 28.6949 - target_loss: 0.0014
Epoch 5/20
10/10 [==============================] - 153s 15s/step - loss: 28.4143 - box_loss: 28.4143 - target_loss: 6.0349e-08
Epoch 6/20
10/10 [==============================] - 158s 16s/step - loss: 27.1531 - box_loss: 27.1531 - target_loss: 2.0451e-07
Epoch 7/20
10/10 [==============================] - 62s 5s/step - loss: 22.2554 - box_loss: 22.2548 - target_loss: 6.0339e-04
Epoch 8/20
10/10 [==============================] - 3s 350ms/step - loss: 27.1756 - box_loss: 27.1756 - target_loss: 1.8626e-09
Epoch 9/20
10/10 [==============================] - 3s 344ms/step - loss: 22.8763 - box_loss: 22.8763 - target_loss: 0.0000e+00
Epoch 10/20
10/10 [==============================] - 3s 344ms/step - loss: 23.6116 - box_loss: 23.5324 - target_loss: 0.0792
Epoch 11/20
10/10 [==============================] - 3s 351ms/step - loss: 23.6213 - box_loss: 23.6213 - target_loss: 6.5937e-08
Epoch 12/20
10/10 [==============================] - 3s 349ms/step - loss: 20.9325 - box_loss: 20.9325 - target_loss: 9.6111e-08
Epoch 13/20
10/10 [==============================] - 3s 342ms/step - loss: 19.7643 - box_loss: 19.7643 - target_loss: 6.2536e-09
Epoch 14/20
10/10 [==============================] - 3s 346ms/step - loss: 21.9087 - box_loss: 21.9071 - target_loss: 0.0016
Epoch 15/20
10/10 [==============================] - 3s 348ms/step - loss: 19.8910 - box_loss: 19.8445 - target_loss: 0.0465
Epoch 16/20
10/10 [==============================] - 3s 341ms/step - loss: 18.0794 - box_loss: 18.0794 - target_loss: 2.2352e-09
Epoch 17/20
10/10 [==============================] - 3s 349ms/step - loss: 19.9182 - box_loss: 19.9182 - target_loss: 0.0000e+00
Epoch 18/20
10/10 [==============================] - 3s 337ms/step - loss: 18.0254 - box_loss: 18.0253 - target_loss: 8.1338e-05
Epoch 19/20
10/10 [==============================] - 3s 339ms/step - loss: 19.9694 - box_loss: 19.9694 - target_loss: 1.5977e-06
Epoch 20/20
10/10 [==============================] - 3s 331ms/step - loss: 17.7530 - box_loss: 17.7530 - target_loss: 3.9085e-10

model.summary()
#Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
lambda (Lambda)                 (None, 224, 224, 3)  0           input_2[0][0]                    
__________________________________________________________________________________________________
vgg16 (Functional)              (None, 7, 7, 512)    14714688    lambda[0][0]                     
__________________________________________________________________________________________________
flatten (Flatten)               (None, 25088)        0           vgg16[0][0]                      
__________________________________________________________________________________________________
dense (Dense)                   (None, 256)          6422784     flatten[0][0]                    
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 128)          32896       dense[0][0]                      
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 64)           8256        dense_1[0][0]                    
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 64)           8256        dense_1[0][0]                    
__________________________________________________________________________________________________
box (Dense)                     (None, 4)            260         dense_2[0][0]                    
__________________________________________________________________________________________________
target (Dense)                  (None, 3)            195         dense_3[0][0]                    
==================================================================================================
Total params: 21,187,335
Trainable params: 6,472,647
Non-trainable params: 14,714,688
__________________________________________________________________________________________________

tf.keras.utils.plot_model(model, rankdir='BT')

history = model.fit(dfg, epochs=20, steps_per_epoch=20)

Epoch 1/20
20/20 [==============================] - 7s 352ms/step - loss: 17.1495 - box_loss: 17.1495 - target_loss: 8.9183e-06
Epoch 2/20
20/20 [==============================] - 7s 349ms/step - loss: 16.8312 - box_loss: 16.8311 - target_loss: 1.7778e-05
Epoch 3/20
20/20 [==============================] - 7s 338ms/step - loss: 16.1163 - box_loss: 16.1148 - target_loss: 0.0015
Epoch 4/20
20/20 [==============================] - 7s 346ms/step - loss: 16.0480 - box_loss: 16.0480 - target_loss: 0.0000e+00
Epoch 5/20
20/20 [==============================] - 7s 349ms/step - loss: 16.7372 - box_loss: 16.7371 - target_loss: 1.0930e-04
Epoch 6/20
20/20 [==============================] - 7s 344ms/step - loss: 15.5043 - box_loss: 15.4865 - target_loss: 0.0177
Epoch 7/20
20/20 [==============================] - 7s 347ms/step - loss: 14.6377 - box_loss: 14.6377 - target_loss: 9.2198e-08
Epoch 8/20
20/20 [==============================] - 7s 343ms/step - loss: 14.4987 - box_loss: 14.4818 - target_loss: 0.0169
Epoch 9/20
20/20 [==============================] - 7s 344ms/step - loss: 14.9790 - box_loss: 14.9560 - target_loss: 0.0229
Epoch 10/20
20/20 [==============================] - 7s 340ms/step - loss: 13.8011 - box_loss: 13.8011 - target_loss: 0.0000e+00
Epoch 11/20
20/20 [==============================] - 7s 349ms/step - loss: 14.2557 - box_loss: 14.2554 - target_loss: 2.6677e-04
Epoch 12/20
20/20 [==============================] - 7s 338ms/step - loss: 12.9601 - box_loss: 12.9229 - target_loss: 0.0371
Epoch 13/20
20/20 [==============================] - 7s 343ms/step - loss: 13.7806 - box_loss: 13.7806 - target_loss: 0.0000e+00
Epoch 14/20
20/20 [==============================] - 7s 337ms/step - loss: 12.5579 - box_loss: 12.5379 - target_loss: 0.0200
Epoch 15/20
20/20 [==============================] - 7s 343ms/step - loss: 14.1479 - box_loss: 14.1479 - target_loss: 2.3356e-07
Epoch 16/20
20/20 [==============================] - 7s 342ms/step - loss: 12.5556 - box_loss: 12.5556 - target_loss: 3.8147e-10
Epoch 17/20
20/20 [==============================] - 7s 344ms/step - loss: 12.5946 - box_loss: 12.5944 - target_loss: 2.0212e-04
Epoch 18/20
20/20 [==============================] - 7s 340ms/step - loss: 11.9439 - box_loss: 11.9369 - target_loss: 0.0070
Epoch 19/20
20/20 [==============================] - 7s 337ms/step - loss: 12.3697 - box_loss: 12.3697 - target_loss: 3.1069e-06
Epoch 20/20
20/20 [==============================] - 7s 341ms/step - loss: 11.9605 - box_loss: 11.9605 - target_loss: 4.5287e-05

pd.DataFrame(history.history).plot.line()

im = tf.keras.preprocessing.image.load_img('dataset/images/airplane/image_0001.jpg')

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpt

bim = np.array(im.resize((224,224,)))[tf.newaxis]

model(bim)
# [<tf.Tensor: shape=(1, 4), dtype=float32, numpy=array([[ 42.0325  ,  28.545605, 291.0303  , 110.72619 ]], dtype=float32)>,
#  <tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[1.0000000e+00, 1.5445283e-31, 0.0000000e+00]], dtype=float32)>]

fig, ax = plt.subplots(1,1)
ax.imshow(im)
pt = mpt.Rectangle((42.0325, 28.545605),291.0303-42.0325,110.72619-28.545605,fill=False)
ax.add_patch(pt)

!pip install -U tensorflow-hub

모델 가져다 쓰는 3가지

1. tf.keras.applications

2. tensorflow hub

3. model garden

import tensorflow_hub as hub # 공개된 모델을 사용할 수 있는 Package

model = tf.keras.models.Sequential([
    hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4', trainable=True, 
                   input_shape=(224,224,3))
])
# 주소에 classification가 있으면 classification전용이고 build하지 않아도 모델 안에 넣을 수 있다 (일반적으로) / 뒤에 있는 번호는 버전 

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer_1 (KerasLayer)   (None, 1001)              3540265   
=================================================================
Total params: 3,540,265
Trainable params: 3,506,153
Non-trainable params: 34,112
_________________________________________________________________

model = tf.keras.models.Sequential([
    hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4', trainable=True,
                   input_shape=(224,224,3))
])

model.summary()

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer_3 (KerasLayer)   (None, 1280)              2257984   
=================================================================
Total params: 2,257,984
Trainable params: 2,223,872
Non-trainable params: 34,112
_________________________________________________________________

mv2 = tf.keras.applications.MobileNetV2(include_top=True)

mv2.summary()
Model: "mobilenetv2_1.00_224"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
Conv1 (Conv2D)                  (None, 112, 112, 32) 864         input_3[0][0]                    
__________________________________________________________________________________________________
bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      
__________________________________________________________________________________________________
Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   
__________________________________________________________________________________________________
expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 
__________________________________________________________________________________________________
expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    
__________________________________________________________________________________________________
expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] 
__________________________________________________________________________________________________
expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0
__________________________________________________________________________________________________
expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      
__________________________________________________________________________________________________
block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   
__________________________________________________________________________________________________
block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             
__________________________________________________________________________________________________
block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          
__________________________________________________________________________________________________
block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        
__________________________________________________________________________________________________
block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                
__________________________________________________________________________________________________
block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          
__________________________________________________________________________________________________
block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            
__________________________________________________________________________________________________
block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         
__________________________________________________________________________________________________
block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             
__________________________________________________________________________________________________
block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          
__________________________________________________________________________________________________
block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        
__________________________________________________________________________________________________
block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          
__________________________________________________________________________________________________
block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            
__________________________________________________________________________________________________
block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         
                                                                 block_2_project_BN[0][0]         
__________________________________________________________________________________________________
block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                
__________________________________________________________________________________________________
block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             
__________________________________________________________________________________________________
block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          
__________________________________________________________________________________________________
block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        
__________________________________________________________________________________________________
block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                
__________________________________________________________________________________________________
block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          
__________________________________________________________________________________________________
block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            
__________________________________________________________________________________________________
block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         
__________________________________________________________________________________________________
block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             
__________________________________________________________________________________________________
block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          
__________________________________________________________________________________________________
block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        
__________________________________________________________________________________________________
block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          
__________________________________________________________________________________________________
block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            
__________________________________________________________________________________________________
block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         
                                                                 block_4_project_BN[0][0]         
__________________________________________________________________________________________________
block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                
__________________________________________________________________________________________________
block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             
__________________________________________________________________________________________________
block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          
__________________________________________________________________________________________________
block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        
__________________________________________________________________________________________________
block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          
__________________________________________________________________________________________________
block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            
__________________________________________________________________________________________________
block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                
                                                                 block_5_project_BN[0][0]         
__________________________________________________________________________________________________
block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                
__________________________________________________________________________________________________
block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             
__________________________________________________________________________________________________
block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          
__________________________________________________________________________________________________
block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        
__________________________________________________________________________________________________
block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                
__________________________________________________________________________________________________
block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          
__________________________________________________________________________________________________
block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            
__________________________________________________________________________________________________
block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         
__________________________________________________________________________________________________
block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             
__________________________________________________________________________________________________
block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          
__________________________________________________________________________________________________
block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        
__________________________________________________________________________________________________
block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          
__________________________________________________________________________________________________
block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            
__________________________________________________________________________________________________
block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         
                                                                 block_7_project_BN[0][0]         
__________________________________________________________________________________________________
block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                
__________________________________________________________________________________________________
block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             
__________________________________________________________________________________________________
block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          
__________________________________________________________________________________________________
block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        
__________________________________________________________________________________________________
block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          
__________________________________________________________________________________________________
block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            
__________________________________________________________________________________________________
block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                
                                                                 block_8_project_BN[0][0]         
__________________________________________________________________________________________________
block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                
__________________________________________________________________________________________________
block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             
__________________________________________________________________________________________________
block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          
__________________________________________________________________________________________________
block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        
__________________________________________________________________________________________________
block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          
__________________________________________________________________________________________________
block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       
__________________________________________________________________________________________________
block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     
__________________________________________________________________________________________________
block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            
__________________________________________________________________________________________________
block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                
                                                                 block_9_project_BN[0][0]         
__________________________________________________________________________________________________
block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                
__________________________________________________________________________________________________
block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            
__________________________________________________________________________________________________
block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         
__________________________________________________________________________________________________
block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       
__________________________________________________________________________________________________
block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         
__________________________________________________________________________________________________
block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           
__________________________________________________________________________________________________
block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        
__________________________________________________________________________________________________
block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            
__________________________________________________________________________________________________
block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         
__________________________________________________________________________________________________
block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       
__________________________________________________________________________________________________
block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         
__________________________________________________________________________________________________
block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           
__________________________________________________________________________________________________
block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        
                                                                 block_11_project_BN[0][0]        
__________________________________________________________________________________________________
block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               
__________________________________________________________________________________________________
block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            
__________________________________________________________________________________________________
block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         
__________________________________________________________________________________________________
block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       
__________________________________________________________________________________________________
block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         
__________________________________________________________________________________________________
block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           
__________________________________________________________________________________________________
block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               
                                                                 block_12_project_BN[0][0]        
__________________________________________________________________________________________________
block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               
__________________________________________________________________________________________________
block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            
__________________________________________________________________________________________________
block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         
__________________________________________________________________________________________________
block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       
__________________________________________________________________________________________________
block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               
__________________________________________________________________________________________________
block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         
__________________________________________________________________________________________________
block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           
__________________________________________________________________________________________________
block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        
__________________________________________________________________________________________________
block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            
__________________________________________________________________________________________________
block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         
__________________________________________________________________________________________________
block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       
__________________________________________________________________________________________________
block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         
__________________________________________________________________________________________________
block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           
__________________________________________________________________________________________________
block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        
                                                                 block_14_project_BN[0][0]        
__________________________________________________________________________________________________
block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               
__________________________________________________________________________________________________
block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            
__________________________________________________________________________________________________
block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         
__________________________________________________________________________________________________
block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       
__________________________________________________________________________________________________
block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         
__________________________________________________________________________________________________
block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           
__________________________________________________________________________________________________
block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               
                                                                 block_15_project_BN[0][0]        
__________________________________________________________________________________________________
block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               
__________________________________________________________________________________________________
block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            
__________________________________________________________________________________________________
block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         
__________________________________________________________________________________________________
block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       
__________________________________________________________________________________________________
block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         
__________________________________________________________________________________________________
block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      
__________________________________________________________________________________________________
block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    
__________________________________________________________________________________________________
block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           
__________________________________________________________________________________________________
Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        
__________________________________________________________________________________________________
Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     
__________________________________________________________________________________________________
out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 1280)         0           out_relu[0][0]                   
__________________________________________________________________________________________________
predictions (Dense)             (None, 1000)         1281000     global_average_pooling2d[0][0]   
==================================================================================================
Total params: 3,538,984
Trainable params: 3,504,872
Non-trainable params: 34,112
__________________________________________________________________________________________________

Localization

객체라고 판단되는 곳에 직사각형(bounding box)를 그려주는 작업
학습을 통해 위치를 찾는다

학습 데이터는 이미지의 target(label)과 rectangle(x,y좌표, 가로,세로)로 구성되어 있다

import matplotlib.pyplot as plt
import matplotlib.patches as mpt
import pandas as pd
import tensorflow as tf
import numpy as np
from PIL import Image 

air = pd.read_csv('dataset/annotations/airplane.csv', header=None)
face = pd.read_csv('dataset/annotations/face.csv', header=None)
motorcycle = pd.read_csv('dataset/annotations/motorcycle.csv', header=None)

air.rename(columns={1:'x1',2:'y1',3:'x2',4:'y2',0:'filename',5:'target'}, inplace=True)
face.rename(columns={1:'x1',2:'y1',3:'x2',4:'y2',0:'filename',5:'target'}, inplace=True)
motorcycle.rename(columns={1:'x1',2:'y1',3:'x2',4:'y2',0:'filename',5:'target'}, inplace=True)

air.filename = air.filename.map(lambda x: 'dataset/images/airplane/'+x)
face.filename = face.filename.map(lambda x: 'dataset/images/face/'+x)
motorcycle.filename = motorcycle.filename.map(lambda x: 'dataset/images/motorcycle/'+x)

data=pd.concat([air,face,motorcycle], axis=0, ignore_index=True)

air.head()

	filename	x1	y1	x2	y2	target
0	image_0001.jpg	49	30	349	137	airplane
1	image_0002.jpg	59	35	342	153	airplane
2	image_0003.jpg	47	36	331	135	airplane
3	image_0004.jpg	47	24	342	141	airplane
4	image_0005.jpg	48	18	339	146	airplane

im = plt.imread('dataset/images/airplane/image_0001.jpg')

fig, ax = plt.subplots(1,1)
ax.imshow(im)
pt = mpt.Rectangle((49,30),349-49,137-30, fill=False) 
ax.add_patch(pt)

data.target.value_counts() # imbalanced data

# airplane      800
# motorcycle    798
# face          435
# Name: target, dtype: int64

im = plt.imread(data.loc[0, 'filename'])

def show_images(i):
  im = plt.imread(data.loc[i, 'filename'])
  fig, ax = plt.subplots(1,1)
  ax.imshow(im)
  pt = mpt.Rectangle((data.loc[i,'x1'],data.loc[i,'y1']),
                     data.loc[i,'x2']-data.loc[i,'x1'],
                     data.loc[i,'y2']-data.loc[i,'y1'], fill=False) 
  ax.add_patch(pt)
  
  show_images(832)

One-hot encoding 3가지 방법

1. scikit learn - onehotencoder

2. pandas - get_dummies

3. data['name'] = (data.target=='name')*1

4. tf.keras.utils.to_categorical

# label encoding이 되어 있는 것만 바꿀 수 있다

from sklearn.preprocessing import OneHotEncoder

ohe = OneHotEncoder()
ohe.fit_transform(data[['target']]).toarray()

array([[1., 0., 0.],
       [1., 0., 0.],
       [1., 0., 0.],
       ...,
       [0., 0., 1.],
       [0., 0., 1.],
       [0., 0., 1.]])

pd.get_dummies(data.target)

	airplane	face	motorcycle
0	1	0	0
1	1	0	0
2	1	0	0
3	1	0	0
4	1	0	0
...	...	...	...
2028	0	0	1
2029	0	0	1
2030	0	0	1
2031	0	0	1
2032	0	0	1

data['airplane'] = (data.target=='airplane')*1
data['face'] = (data.target=='face')*1
data['motorcycle'] = (data.target=='motorcycle')*1

data.tail().filename
# 2028    dataset/images/motorcycle/image_0794.jpg
# 2029    dataset/images/motorcycle/image_0795.jpg
# 2030    dataset/images/motorcycle/image_0796.jpg
# 2031    dataset/images/motorcycle/image_0797.jpg
# 2032    dataset/images/motorcycle/image_0798.jpg
# Name: filename, dtype: object

# data['label']=data.target.map({'airplane':0, 'face':1, 'motorcycle':2})
tf.keras.utils.to_categorical(data.label)
# array([[1., 0., 0.],
       [1., 0., 0.],
       [1., 0., 0.],
       ...,
       [0., 0., 1.],
       [0., 0., 1.],
       [0., 0., 1.]], dtype=float32)

data.drop(columns=['target','label'], inplace=True)

Label encoding

1. map

2. scikit-learn - labelencoder

data['label']=data.target.map({'airplane':0, 'face':1, 'motorcycle:':2})

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit_transform(data.target)
# array([0, 0, 0, ..., 2, 2, 2])

data.values
array([['dataset/images/airplane/image_0001.jpg', 49, 30, ..., 1, 0, 0],
       ['dataset/images/airplane/image_0002.jpg', 59, 35, ..., 1, 0, 0],
       ['dataset/images/airplane/image_0003.jpg', 47, 36, ..., 1, 0, 0],
       ...,
       ['dataset/images/motorcycle/image_0796.jpg', 47, 40, ..., 0, 0, 1],
       ['dataset/images/motorcycle/image_0797.jpg', 48, 54, ..., 0, 0, 1],
       ['dataset/images/motorcycle/image_0798.jpg', 42, 33, ..., 0, 0, 1]],
      dtype=object)

이미지 한꺼번에 불러오기

1. tf.keras.preprocessing.image_dataset_from_directory => tf.data.Dataset으로 불러온다

2. tf.keras.preprocessing.image.ImageDataGenerator => numpy로 불러온다 / augmentation과 파일 저장을 옵션으로 사용할 수 있다

data.head()

filename	x1	y1	x2	y2	airplane	face	motorcycle
0	dataset/images/airplane/image_0001.jpg	49	30	349	137	1	0	0
1	dataset/images/airplane/image_0002.jpg	59	35	342	153	1	0	0
2	dataset/images/airplane/image_0003.jpg	47	36	331	135	1	0	0
3	dataset/images/airplane/image_0004.jpg	47	24	342	141	1	0	0
4	dataset/images/airplane/image_0005.jpg	48	18	339	146	1	0	0

idg = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255) # normalization하면서 불러온다
dg = idg.flow_from_dataframe(data, class_mode='raw', y_col=['x1','x2','y1','y2'], target_size=(64,128)) # resize를 강제시킨다 / 데이터 값을 내가 원하는 형태로 불러올 수 있다 
# localization에 활용하기 좋은 형태로 불러올 수 있다 

ims = next(dg)

ims[1][0], ims[1][1]
# (array([ 82, 366,  56, 171]), array([ 34, 230,  19, 127]))

ims[0][0].shape
# (256, 256, 3)

train = tf.data.Dataset.from_generator(lambda :dg, output_types=(tf.float32,tf.float32))

t = iter(train.take(1))

next(t)
# (<tf.Tensor: shape=(32, 256, 256, 3), dtype=float32, numpy=
 array([[[[1.        , 1.        , 1.        ],
          [1.        , 1.        , 1.        ],
          [1.        , 1.        , 1.        ],
          ...,
         [[0.9960785 , 0.98823535, 0.9921569 ],
          [1.        , 0.9960785 , 1.        ],
          [1.        , 0.9960785 , 0.9921569 ],
          ...,
          [1.        , 1.        , 1.        ],
          [1.        , 1.        , 1.        ],
          [1.        , 1.        , 1.        ]]]], dtype=float32)>,
          <tf.Tensor: shape=(32, 4), dtype=float32, numpy=
 array([[ 45., 345.,  31., 165.],
        [ 38., 238.,  24., 143.],
        [ 48., 351.,  29., 113.],
        [ 49., 209.,  51., 127.],
        [176., 379.,  15., 275.],
        [ 35., 234.,  25., 144.],
        [ 52., 346.,  27., 112.],
        [ 51., 350.,  33., 123.],
        [ 50., 350.,  29., 112.],
        [ 56., 353.,  33., 138.],
        [ 32., 241.,  20., 283.],
        [ 48., 348.,  26.,  92.],
        [ 35., 229.,  22., 151.],
        [ 36., 226.,  23., 129.],
        [ 36., 228.,  60., 174.],
        [ 32., 230.,  21., 118.],
        [ 47., 232.,  26., 136.],
        [ 51., 345.,  29., 132.],
        [171., 379.,  26., 278.],
        [ 59., 350.,  32., 126.],
        [ 36., 227.,  35., 147.],
        [ 24., 349.,  28., 124.],
        [ 29., 228.,  24., 136.],
        [ 32., 229.,  21., 150.],
        [ 35., 227.,  21., 143.],
        [154., 342.,  38., 285.],
        [122., 320.,  16., 253.],
        [ 35., 232.,  15., 132.],
        [ 50., 347.,  27., 119.],
        [ 37., 227.,  32., 164.],
        [ 75., 358.,  42., 153.],
        [ 39., 231.,  27., 140.]], dtype=float32)>)

for i in train.take(1):
  print(i[1])
  
tf.Tensor(
[[ 46. 352.  29. 113.]
 [ 47. 217.  40. 141.]
 [ 34. 228.  18. 121.]
 [266. 462.  54. 324.]
 [207. 391.  20. 282.]
 [ 61. 346.  24. 148.]
 [ 70. 347.  86. 167.]
 [ 33. 228.  17. 127.]
 [ 80. 280.  39. 314.]
 [ 48. 345.  27. 108.]
 [ 35. 229.  20. 126.]
 [ 54. 346.  29. 120.]
 [ 35. 232.  23. 149.]
 [ 61. 350.  27. 113.]
 [ 35. 216.  23. 134.]
 [ 48. 221.  43. 138.]
 [155. 348.  18. 263.]
 [ 52. 349.  28. 116.]
 [160. 375.  28. 304.]
 [ 51. 345.  33. 138.]
 [ 49. 344.  23. 122.]
 [ 55. 345.  22. 140.]
 [ 49. 349.  33. 107.]
 [ 34. 226.  22. 133.]
 [ 62. 353.  67. 127.]
 [ 49. 225.  52. 150.]
 [ 32. 233.  18. 137.]
 [118. 297.  13. 258.]
 [ 43. 344.  31. 117.]
 [ 57. 344.  24. 105.]
 [247. 432.  19. 286.]
 [ 55. 352.  31. 140.]], shape=(32, 4), dtype=float32)

vgg = tf.keras.applications.VGG16(include_top=False)
vgg.trainable = False 

input_ = tf.keras.Input((64,128,3))
x = tf.keras.layers.Lambda(lambda x:tf.keras.applications.vgg16.preprocess_input(x))(input_)
x = vgg(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(4)(x)
model = tf.keras.models.Model(input_, x)

model.compile(loss=tf.keras.losses.MeanAbsoluteError(), metrics=['mae']) 
# Huber는 값이 클 때는 L1 loss, 값이 작을 때는 L2 loss 방식을 쓴다 

model.summary()
Model: "model_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         [(None, 64, 128, 3)]      0         
_________________________________________________________________
lambda_3 (Lambda)            (None, 64, 128, 3)        0         
_________________________________________________________________
vgg16 (Functional)           (None, None, None, 512)   14714688  
_________________________________________________________________
flatten_3 (Flatten)          (None, 4096)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 4)                 16388     
=================================================================
Total params: 14,731,076
Trainable params: 16,388
Non-trainable params: 14,714,688
_________________________________________________________________

model.fit(train, epochs=1, steps_per_epoch=50)
# 50/50 [==============================] - 933s 19s/step - loss: 108.1627 - mae: 108.1627 

model(x[tf.newaxis])
# <tf.Tensor: shape=(1, 4), dtype=float32, numpy=
# array([[-17.944298 ,  15.799198 ,  19.511517 ,   1.9699388]],
#       dtype=float32)>

im = Image.open('dataset/images/airplane/image_0001.jpg')
x = np.array(im.resize((128,64)))

plt.imshow(x)

Fast R-CNN

Localization + Classification을 한방에 처리하는 모델

Localization 문제를 CNN으로 풀수 있지만 Machine learning은 ad-hoc 이라는 특성 때문에

catastrophic forgetting문제와 multi-loss문제로 인해 Localization + classification을 동시에 할 수 없었다

왜냐 하면 여태까지 localization을 Regression으로 해결해왔기 때문에 multiple loss를 사용하면 어느 하나 모델은 굉장히 성능이 떨어지는 문제가 있었다

그리고 classification + localization 문제를 CNN하나로 해결 할 수 있을 거라 생각하지 못 했었다

그런데 CNN은 위치정보와 특성정보를 동시에 갖고 있기 때문에 output을 두 개로 늘려 multiple loss를 동시에 사용해도 학습이 잘 되는 것을 확인할 수 있었다

Object detection

직사각형 안에 무엇이 있는지 찾는 것

Multi-Labeled Classification + Bounding Box Regression(Localization)

여러 물체에 대해 어떤 물체인지 분류하는 Classification문제와 그 물체가 어디 있는지

박스(Bounding box)를 통해 위치 정보를 나타내는 Localization문제 둘 다를 해내야 하는 분야

Object dection paper flow

R-CNN은 object detection 문제에서 CNN을 활용한 최초의 논문이다

Localization

객체라고 판단되는 곳에 직사각형(bounding box)를 그려주는 작업

Segmentation

모든 픽셀의 label을 예측한다

1. Semantic segmentation

- 모든 픽셀을 미리 지정된 개수의 class로 분류하는 것

- 동일한 객체들끼리는 한 번에 masking을 수행한다

2. Instance segmentation

- sematic segmentation와 유사하지만 같은 class이더라도 다른 것으로 분류한다

- 동일한 객체들이여도 개별로 masking을 수행한다

Naive approach

물체가 존재할 수 있는 모든 크기의 영역에 대해 sliding window 방식으로 이미지를 모두 탐색하면서 classificaion을 수행하는 방식
탐색해야 할 영역이 많기 때문에 연산 시간이 오래걸리고 비효율적인 방법이다

Sliding window

일정 크기의 Window를 이미지 위에서 조금씩 옮기면서 값을 비교하는 알고리즘

Region proposals

sliding window 방식의 비효율성을 개선하기 위해 입력 영상에서 '물체가 있을 법한' 영역을 빠른 속도로 찾아내는 알고리즘

Selective search

객체의 주변 간의 색감, 질감 차이, 다른 물체에 둘러 쌓여 있는지 여부 등을 파악해서

다양한 전략으로 물체의 위치를 파악할 수 있도록 하는 알고리즘

Bounding box들을 Random하게 많이 생성을 하고 이들을 조금씩 Merge해나가면서 물체를 인식해 나간다

!pip install selective-search
!pip install selectivesearch
!pip install -U opencv-contrib-python

import selective_search
import selectivesearch

import skimage
from skimage import data
import matplotlib.pyplot as plt
import matplotlib.patches as pts

dir(selective_search)
# ['__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'box_filter',
 'core',
 'measure',
 'selective_search',
 'structure',
 'util']

plt.imshow(data.astronaut())

plt.imshow(data.rocket())

im = data.rocket()

ss = selective_search.selective_search(im) # 좌표값을 list로 찾아준다 

len(ss)
# 489

boxes = selective_search.box_filter(ss, topN=30)

fig, ax = plt.subplots(1,1)
ax.imshow(im)
for b in boxes:
  box = pts.Rectangle((b[0],b[1]), b[2]-b[0], b[3]-b[1], fill=None, edgecolor='red') # xy(원점), width, height
  ax.add_patch(box)

ss2 = selective_search.selective_search(im, mode='fast')

boxes = selective_search.box_filter(ss2, topN=30)

fig, ax = plt.subplots(1,1)
ax.imshow(im)
for b in boxes:
  box = pts.Rectangle((b[0],b[1]), b[2]-b[0], b[3]-b[1], fill=None, edgecolor='red') 
  ax.add_patch(box)

im2 = data.astronaut()

ss = selective_search.selective_search(im2)
boxes = selective_search.box_filter(ss, topN=20)

fig, ax = plt.subplots(1,1)
ax.imshow(im2)
for b in boxes:
  box = pts.Rectangle((b[0],b[1]), b[2]-b[0], b[3]-b[1], fill=None, edgecolor='red') # xy(원점), width, height
  ax.add_patch(box)

sss = selectivesearch.selective_search(im, min_size=500) # numpy로 찾아준다 

sss[1] # 영역 
[{'labels': [0.0], 'rect': (0, 0, 18, 7), 'size': 121},
 {'labels': [1.0], 'rect': (19, 0, 9, 8), 'size': 65},
 {'labels': [2.0], 'rect': (16, 0, 32, 15), 'size': 207},
 {'labels': [3.0], 'rect': (45, 0, 14, 35), 'size': 223},
 {'labels': [4.0], 'rect': (57, 0, 34, 10), 'size': 300},
 {'labels': [5.0], 'rect': (88, 0, 29, 65), 'size': 209},
 {'labels': [6.0], 'rect': (89, 0, 49, 7), 'size': 217},
 {'labels': [7.0], 'rect': (133, 0, 42, 15), 'size': 242},
 {'labels': [8.0], 'rect': (152, 0, 68, 8), 'size': 292},
 {'labels': [9.0], 'rect': (214, 0, 22, 7), 'size': 96},
 {'labels': [10.0], 'rect': (232, 0, 32, 8), 'size': 189},
 {'labels': [11.0], 'rect': (265, 0, 20, 9), 'size': 114},
 {'labels': [12.0], 'rect': (269, 0, 26, 5), 'size': 118},
 {'labels': [13.0], 'rect': (291, 0, 22, 15), 'size': 218},
 {'labels': [14.0], 'rect': (307, 0, 20, 15), 'size': 145},
 {'labels': [15.0], 'rect': (312, 0, 18, 6), 'size': 81},
 {'labels': [16.0], 'rect': (325, 0, 28, 9), 'size': 115},
 {'labels': [17.0], 'rect': (339, 0, 19, 7), 'size': 93},
 {'labels': [18.0], 'rect': (358, 0, 25, 5), 'size': 92},
 {'labels': [19.0], 'rect': (384, 0, 11, 5), 'size': 55},
 {'labels': [20.0], 'rect': (384, 0, 18, 12), 'size': 126},
 {'labels': [21.0], 'rect': (398, 0, 27, 8), 'size': 163},
 {'labels': [22.0], 'rect': (402, 0, 31, 15), 'size': 191},
 {'labels': [23.0], 'rect': (429, 0, 15, 9), 'size': 142},
 {'labels': [24.0], 'rect': (432, 0, 19, 15), 'size': 136},
 {'labels': [25.0], 'rect': (447, 0, 22, 15), 'size': 161},
 {'labels': [26.0], 'rect': (456, 0, 26, 10), 'size': 206},
 {'labels': [27.0], 'rect': (472, 0, 16, 7), 'size': 72},
 {'labels': [28.0], 'rect': (487, 0, 16, 17), 'size': 139},
 {'labels': [29.0], 'rect': (493, 0, 37, 28), 'size': 503},
 {'labels': [30.0], 'rect': (529, 0, 16, 24), 'size': 234},
 {'labels': [31.0], 'rect': (546, 0, 12, 11), 'size': 118},
 {'labels': [32.0], 'rect': (539, 0, 21, 61), 'size': 209},
 {'labels': [33.0], 'rect': (560, 0, 11, 10), 'size': 97},
 {'labels': [34.0], 'rect': (567, 0, 14, 26), 'size': 107},
 {'labels': [35.0], 'rect': (579, 0, 19, 17), 'size': 225},
 {'labels': [36.0], 'rect': (591, 0, 31, 20), 'size': 232},
 {'labels': [37.0], 'rect': (613, 0, 18, 10), 'size': 94},
 {'labels': [38.0], 'rect': (626, 0, 13, 13), 'size': 124},
 {'labels': [39.0], 'rect': (10, 2, 14, 9), 'size': 70},
 {'labels': [40.0], 'rect': (114, 2, 21, 9), 'size': 131},
 {'labels': [41.0], 'rect': (221, 2, 16, 10), 'size': 107},
 {'labels': [42.0], 'rect': (354, 2, 43, 14), 'size': 224},
 {'labels': [43.0], 'rect': (90, 3, 13, 12), 'size': 95},
 {'labels': [44.0], 'rect': (199, 3, 36, 23), 'size': 365},
 {'labels': [45.0], 'rect': (320, 3, 17, 9), 'size': 90},
 {'labels': [46.0], 'rect': (160, 4, 39, 11), 'size': 125},
 {'labels': [47.0], 'rect': (280, 4, 19, 11), 'size': 122},
 {'labels': [48.0], 'rect': (336, 4, 23, 11), 'size': 132},
 {'labels': [49.0], 'rect': (0, 5, 51, 18), 'size': 243},
 {'labels': [50.0], 'rect': (99, 5, 14, 6), 'size': 71},
 {'labels': [51.0], 'rect': (208, 5, 14, 10), 'size': 75},
 {'labels': [52.0], 'rect': (236, 5, 33, 7), 'size': 124},
 {'labels': [53.0], 'rect': (479, 5, 25, 18), 'size': 184},
 {'labels': [54.0], 'rect': (496, 5, 23, 17), 'size': 171},
 {'labels': [55.0], 'rect': (89, 6, 22, 22), 'size': 280},
 {'labels': [56.0], 'rect': (104, 6, 44, 17), 'size': 402},
 {'labels': [57.0], 'rect': (169, 6, 29, 8), 'size': 170},
 {'labels': [58.0], 'rect': (600, 6, 21, 40), 'size': 270},
 {'labels': [59.0], 'rect': (0, 7, 15, 11), 'size': 129},
 {'labels': [60.0], 'rect': (358, 7, 15, 5), 'size': 60},
 {'labels': [61.0], 'rect': (549, 7, 9, 16), 'size': 67},
 {'labels': [62.0], 'rect': (574, 7, 26, 27), 'size': 140},
 {'labels': [63.0], 'rect': (146, 8, 22, 14), 'size': 165},
 {'labels': [64.0], 'rect': (160, 8, 35, 12), 'size': 163},
 {'labels': [65.0], 'rect': (238, 8, 47, 9), 'size': 241},
 {'labels': [66.0], 'rect': (470, 8, 15, 12), 'size': 81},
 {'labels': [67.0], 'rect': (527, 8, 9, 13), 'size': 69},
 {'labels': [68.0], 'rect': (576, 8, 13, 20), 'size': 139},
 {'labels': [69.0], 'rect': (62, 9, 25, 45), 'size': 226},
 {'labels': [70.0], 'rect': (540, 9, 12, 6), 'size': 62},
 {'labels': [71.0], 'rect': (560, 9, 11, 46), 'size': 175},
 {'labels': [72.0], 'rect': (611, 9, 20, 15), 'size': 104},
 {'labels': [73.0], 'rect': (622, 9, 17, 14), 'size': 149},
 {'labels': [74.0], 'rect': (31, 10, 19, 20), 'size': 134},
 {'labels': [75.0], 'rect': (53, 10, 15, 30), 'size': 195},
 {'labels': [76.0], 'rect': (342, 10, 44, 13), 'size': 351},
 {'labels': [77.0], 'rect': (394, 10, 38, 6), 'size': 102},
 {'labels': [78.0], 'rect': (454, 11, 24, 10), 'size': 131},
 {'labels': [79.0], 'rect': (62, 12, 22, 21), 'size': 273},
 {'labels': [80.0], 'rect': (313, 12, 30, 11), 'size': 232},
 {'labels': [81.0], 'rect': (544, 12, 14, 19), 'size': 175},
 {'labels': [82.0], 'rect': (231, 13, 16, 7), 'size': 76},
 {'labels': [83.0], 'rect': (236, 13, 25, 5), 'size': 57},
 {'labels': [84.0], 'rect': (249, 13, 31, 6), 'size': 75},
 {'labels': [85.0], 'rect': (282, 13, 30, 15), 'size': 226},
 {'labels': [86.0], 'rect': (408, 13, 20, 13), 'size': 127},
 {'labels': [87.0], 'rect': (440, 13, 17, 12), 'size': 97},
 {'labels': [88.0], 'rect': (156, 14, 56, 13), 'size': 233},
 {'labels': [89.0], 'rect': (296, 14, 17, 9), 'size': 122},
 {'labels': [90.0], 'rect': (392, 14, 16, 9), 'size': 101},
 {'labels': [91.0], 'rect': (588, 14, 9, 13), 'size': 76},
 {'labels': [92.0], 'rect': (429, 15, 10, 8), 'size': 56},
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