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!git clone --depth 1 https://github.com/google/automl
!cd /content/automl/efficientdet; pip install -r requirements.txt
import tensorflow as tf
print(tf.__version__)
# 2.7.0
!nvidia-smi
Tue Nov 30 09:31:28 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 495.44 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |
| N/A 36C P8 28W / 149W | 0MiB / 11441MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
efficientdet 수행을 위한 Library path 설정.
- efficientdet을 setup을 하지 않고, 소스코드를 sys.path.append('/content/automl/efficientdet')로 library path 설정.
import os
import sys
import tensorflow.compat.v1 as tf
sys.path.append('/content/automl/efficientdet')
# /content/automl/efficient 으로 library path가 정상적으로 잡히면 아래 모듈 import가 되어야함.
import hparams_config
from tf2 import anchors # keras folder가 tf2로 변경됨
from model_inspect import ModelInspectorCOCO 데이터로 Pretrained된 efficientdet-d0 모델을 다운로드
MODEL = 'efficientdet-d0'
def download(m):
if m not in os.listdir():
!wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/{m}.tar.gz
!tar zxf {m}.tar.gz
ckpt_path = os.path.join(os.getcwd(), m)
return ckpt_path
# Download checkpoint.
ckpt_path = download(MODEL)
print('Use model in {}'.format(ckpt_path))
Use model in /content/efficientdet-d0
!mkdir ./data
!wget -O ./data/img01.png https://user-images.githubusercontent.com/11736571/77320690-099af300-6d37-11ea-9d86-24f14dc2d540.png
import cv2
import matplotlib.pyplot as plt
image_array = cv2.cvtColor(cv2.imread('/content/data/img01.png'), cv2.COLOR_BGR2RGB)
print(image_array.shape)
plt.figure(figsize=(12, 12))
plt.imshow(image_array)
# (1080, 1920, 3)
Pretrained efficientdet 모델로 Inference 를 수행하기 위한 환경 설정
- hparams_config.Config 객체를 통해 모델 환경 설정.
class INFER_CFG:
model_name = 'efficientdet-d0' # efficientdet 모델명
model_dir = '/content/efficientdet-d0' # pretrained checkpoint 파일이 있는 디렉토리
hparams = '' # csv 형식의 k=v 쌍 또는 yaml file
import numpy as np
from PIL import Image
import tensorflow as tf
import hparams_config
import inference
from tf2 import efficientdet_keras # keras 를 tf2로 변경
#efficientdet-d0의 기본 config 확인.
config = hparams_config.get_efficientdet_config(INFER_CFG.model_name)
print('config type:', type(config))
print(config)
# config 객체, model parameter 등 정보들
config type: <class 'hparams_config.Config'>
act_type: swish
alpha: 0.25
anchor_scale: 4.0
apply_bn_for_resampling: true
aspect_ratios:
- 1.0
- 2.0
- 0.5
autoaugment_policy: null
backbone_config: null
backbone_name: efficientnet-b0
box_class_repeats: 3
box_loss_weight: 50.0
ckpt_var_scope: null
clip_gradients_norm: 10.0
conv_after_downsample: false
conv_bn_act_pattern: false
data_format: channels_last
dataset_type: null
delta: 0.1
drop_remainder: true
first_lr_drop_epoch: 200.0
fpn_cell_repeats: 3
fpn_config: null
fpn_name: null
fpn_num_filters: 64
fpn_weight_method: null
gamma: 1.5
grad_checkpoint: false
grid_mask: false
heads:
- object_detection
image_size: 512
img_summary_steps: null
input_rand_hflip: true
iou_loss_type: null
iou_loss_weight: 1.0
is_training_bn: true
jitter_max: 2.0
jitter_min: 0.1
label_map: null
label_smoothing: 0.0
learning_rate: 0.08
loss_scale: null
lr_decay_method: cosine
lr_warmup_epoch: 1.0
lr_warmup_init: 0.008
map_freq: 5
max_instances_per_image: 100
max_level: 7
mean_rgb:
- 123.675
- 116.28
- 103.53
min_level: 3
mixed_precision: false
model_optimizations: {}
momentum: 0.9
moving_average_decay: 0.9998
name: efficientdet-d0
nms_configs:
iou_thresh: null
max_nms_inputs: 0
max_output_size: 100
method: gaussian
pyfunc: false
score_thresh: 0.0
sigma: null
num_classes: 90
num_epochs: 300
num_scales: 3
optimizer: sgd
poly_lr_power: 0.9
positives_momentum: null
regenerate_source_id: false
sample_image: null
save_freq: epoch
scale_range: false
second_lr_drop_epoch: 250.0
seg_num_classes: 3
separable_conv: true
skip_crowd_during_training: true
skip_mismatch: true
stddev_rgb:
- 58.395
- 57.120000000000005
- 57.375
strategy: null
survival_prob: null
target_size: null
tflite_max_detections: 100
use_keras_model: true
var_freeze_expr: null
verbose: 1
weight_decay: 4.0e-05
# config의 특정 항목을 update
config.is_training_bn = False # batch_normalization 옵션인데 학습이 아닌 inference라서 false
#config.image_size = '1920x1280'
config.nms_configs.score_thresh = 0.4 # nms의 threshold filtering 0.4이하는 아웃시킴
config.nms_configs.max_output_size = 100
config.override(INFER_CFG.hparams)Pretrained 모델 생성 후 Inference 수행
- config인자로 하여 EfficientDetModel생성
- 만들어진 모델에 다운로드 된 Pretrained Weight 파일의 weight값을 model.load_weights()로 입력
INFER_CFG.model_dir
/content/efficientdet-d0
import inference
from tf2 import efficientdet_keras # keras를 tf2로 변경
# 빈 모델,random weight 상태
model = efficientdet_keras.EfficientDetModel(config=config)
model.build((None, None, None, 3)) # input layer가 개수, h,w가 정해지지 않음, 채널은 3
print('#### checkpoint name:', tf.train.latest_checkpoint(INFER_CFG.model_dir))
# load_weight로 weights 채움
model.load_weights(tf.train.latest_checkpoint(INFER_CFG.model_dir))
model.summary()
Model: ""
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
efficientnet-b0 (Model) multiple 3634844 # backbone : b1
resample_p6 (ResampleFeatur multiple 20800 # neck : BiFPN
eMap)
resample_p7 (ResampleFeatur multiple 0
eMap)
fpn_cells (FPNCells) multiple 179321
class_net (ClassNet) multiple 71274 # head
box_net (BoxNet) multiple 20964
=================================================================
Total params: 3,927,203
Trainable params: 3,880,067
Non-trainable params: 47,136
_________________________________________________________________입력
from PIL import Image
import cv2
# image는 4차원 array, Tensor 모두 가능.
imgs = [np.array(Image.open('/content/data/img01.png'))]
imgs = tf.convert_to_tensor(imgs, dtype=tf.uint8)
### 아래와 같이 numpy array도 모델에 입력되는 이미지 값으로 가능.
'''
img = cv2.cvtColor(cv2.imread('/content/data/img01.png'), cv2.COLOR_BGR2RGB)
imgs= img[np.newaxis, ...]
boxes, scores, classes, valid_len = model(imgs, training=False, post_mode='global')
'''
print()
import time
# Inference 수행하고 수행 시간을 측정.
start_time = time.time()
boxes, scores, classes, valid_len = model(imgs, training=False, post_mode='global')
print('elapsed time:', time.time() - start_time)
/content/automl/efficientdet/utils.py:255: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
for u in self.updates:
elapsed time: 7.936372518539429Inference 반환 결과 살펴보고 API로 시각화 하기
- inference model에 image tensor를 입력하여 반환된 결과는 모두 tensor이며, bounding box의 좌표, confidence score, class id 값, valid한 갯수가 반환됨.
- config에 max_instances_per_image이 100으로 설정되었으므로 기본적으로 inference결과는 100개의 object들의 Detection 결과를 가지게 됨.
- 이들 중 valid한 갯수(valid_len)은 이들중 의미있는 object detection 갯수를 의미함.(0 부터 valid_len-1 까지의 index를 가진 array결과가 의미있는 detection 결과임)
- inference.visualize_image()로 반화 결과를 입력하여 시각화 적용
boxes
<tf.Tensor: shape=(1, 100, 4), dtype=float32, numpy=
array([[[ 898.7691 , 1250.5381 , 1078.944 , 1593.3597 ],
[ 485.65512 , 1178.6895 , 613.15283 , 1317.9205 ],
[ 620.43 , 1313.1947 , 905.7822 , 1625.1599 ],
[ 870.51105 , 490.04422 , 1078.4515 , 778.78973 ],
[ 505.97702 , 1356.5035 , 636.8839 , 1533.2777 ],
[ 628.0882 , 211.97603 , 842.73444 , 485.74365 ],
[ 648.43634 , 1553.6306 , 911.2138 , 1901.6282 ],
[ 875.3995 , 3.3257744, 1039.7441 , 182.6283 ],
[ 925.4872 , 301.89096 , 1075.1144 , 460.31177 ],
[ 619.0373 , 873.7107 , 927.9857 , 1069.7871 ],
[ 351.75012 , 582.6734 , 511.38934 , 738.4224 ],
[ 690.2022 , 1238.6897 , 860.37756 , 1316.6832 ],
[ 677.63 , 1087.3639 , 835.46027 , 1159.2014 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ],
[ 677.3427 , 1864.105 , 759.21936 , 1864.105 ]]],
dtype=float32)>ㄴ> 있든 없든 max_instances_per_image 100개를 다 채우려고 중복값이 나옴
valid_len이 13임
print(valid_len.numpy())
boxes.shape, scores.shape, classes.shape
# [13]
# (TensorShape([1, 100, 4]), TensorShape([1, 100]), TensorShape([1, 100]))
scores
<tf.Tensor: shape=(1, 100), dtype=float32, numpy=
array([[0.84579366, 0.6978561 , 0.68892914, 0.6842166 , 0.67942005,
0.6492443 , 0.61395675, 0.484145 , 0.44828248, 0.43981278,
0.41531536, 0.41527689, 0.41297233, 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. ,
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. ]],
dtype=float32)>object는 13개보다 많을 수 있는데 필터링을 0.4 초과인 것만 찾아낸 것임
print('##bboxes:', boxes[0, :10], '\n##scores:', scores[0, :10], '\n##classes:', classes[0, :10])
##bboxes: tf.Tensor(
[[ 898.7691 1250.5381 1078.944 1593.3597 ]
[ 485.65512 1178.6895 613.15283 1317.9205 ]
[ 620.43 1313.1947 905.7822 1625.1599 ]
[ 870.51105 490.04422 1078.4515 778.78973 ]
[ 505.97702 1356.5035 636.8839 1533.2777 ]
[ 628.0882 211.97603 842.73444 485.74365 ]
[ 648.43634 1553.6306 911.2138 1901.6282 ]
[ 875.3995 3.3257744 1039.7441 182.6283 ]
[ 925.4872 301.89096 1075.1144 460.31177 ]
[ 619.0373 873.7107 927.9857 1069.7871 ]], shape=(10, 4), dtype=float32)
##scores: tf.Tensor(
[0.84579366 0.6978561 0.68892914 0.6842166 0.67942005 0.6492443
0.61395675 0.484145 0.44828248 0.43981278], shape=(10,), dtype=float32)
##classes: tf.Tensor([3. 3. 3. 3. 3. 3. 3. 4. 4. 3.], shape=(10,), dtype=float32)
!mkdir -p /content/data_output
for i, img in enumerate(imgs):
length = valid_len[i]
img = inference.visualize_image(
img,
boxes[i].numpy()[:length],
classes[i].numpy().astype(np.int)[:length],
scores[i].numpy()[:length],
label_map=config.label_map,
min_score_thresh=config.nms_configs.score_thresh,
max_boxes_to_draw=config.nms_configs.max_output_size)
output_image_path = os.path.join('/content/data_output', str(i) + '.jpg')
Image.fromarray(img).save(output_image_path)
print('writing annotated image to %s' % output_image_path)
writing annotated image to /content/data_output/0.jpg
Static Graph mode(Non eager mode)로 Inference 수행 성능 향상 시키기
- @tf.function을 이용하여 static mode로 inference를 수행할 수 있도록 ExportModel 클래스 생성
- inference 수행 시 ExportModel의 @tf.function이 적용된 메소드를 호출할 수 있도록 함.
import time
class ExportModel(tf.Module):
def __init__(self, model):
super().__init__()
self.model = model
@tf.function
def f(self, imgs):
#model(imgs, training=False, post_mode='global')
return self.model(imgs, training=False, post_mode='global')
export_model = ExportModel(model)
# p100에서 image 1920x1280일 경우 74ms, v100에서 image 512x512일 경우 24ms
start_time = time.time()
boxes, scores, classes, valid_len = export_model.f(imgs)
print('elapsed time:', time.time() - start_time)
/content/automl/efficientdet/utils.py:23: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
from tensorflow.python.tpu import tpu_function # pylint:disable=g-direct-tensorflow-import
/content/automl/efficientdet/utils.py:255: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
for u in self.updates:
elapsed time: 8.002159118652344시각화 함수 생성하고 inference 결과를 시각화
labels_to_names = {1:'person',2:'bicycle',3:'car',4:'motorcycle',5:'airplane',6:'bus',7:'train',8:'truck',9:'boat',10:'traffic light',
11:'fire hydrant',12:'street sign',13:'stop sign',14:'parking meter',15:'bench',16:'bird',17:'cat',18:'dog',19:'horse',20:'sheep',
21:'cow',22:'elephant',23:'bear',24:'zebra',25:'giraffe',26:'hat',27:'backpack',28:'umbrella',29:'shoe',30:'eye glasses',
31:'handbag',32:'tie',33:'suitcase',34:'frisbee',35:'skis',36:'snowboard',37:'sports ball',38:'kite',39:'baseball bat',40:'baseball glove',
41:'skateboard',42:'surfboard',43:'tennis racket',44:'bottle',45:'plate',46:'wine glass',47:'cup',48:'fork',49:'knife',50:'spoon',
51:'bowl',52:'banana',53:'apple',54:'sandwich',55:'orange',56:'broccoli',57:'carrot',58:'hot dog',59:'pizza',60:'donut',
61:'cake',62:'chair',63:'couch',64:'potted plant',65:'bed',66:'mirror',67:'dining table',68:'window',69:'desk',70:'toilet',
71:'door',72:'tv',73:'laptop',74:'mouse',75:'remote',76:'keyboard',77:'cell phone',78:'microwave',79:'oven',80:'toaster',
81:'sink',82:'refrigerator',83:'blender',84:'book',85:'clock',86:'vase',87:'scissors',88:'teddy bear',89:'hair drier',90:'toothbrush',
91:'hair brush'}
def get_detected_img(export_model, img_array, is_print=True):
# automl efficent은 반환 bbox 좌표값이 원본 이미지 좌표값으로 되어 있으므로 별도의 scaling작업 필요 없음.
'''
height = img_array.shape[0]
width = img_array.shape[1]
'''
# cv2의 rectangle()은 인자로 들어온 이미지 배열에 직접 사각형을 업데이트 하므로 그림 표현을 위한 별도의 이미지 배열 생성.
draw_img = img_array.copy()
# bounding box의 테두리와 caption 글자색 지정
green_color=(0, 255, 0)
red_color=(0, 0, 255)
# cv2로 만들어진 numpy image array를 tensor로 변환
img_tensor = tf.convert_to_tensor(img_array, dtype=tf.uint8)[tf.newaxis, ...]
#img_tensor = tf.convert_to_tensor(img_array, dtype=tf.float32)[tf.newaxis, ...]
# efficientdet 모델을 다운로드 한 뒤 inference 수행.
start_time = time.time()
# automl efficientdet 모델은 boxes, score, classes, num_detections를 각각 Tensor로 반환.
boxes, scores, classes, valid_len = export_model.f(img_tensor)
# Tensor값을 시각화를 위해 numpy 로 변환.
boxes = boxes.numpy()
scores = scores.numpy()
classes = classes.numpy()
valid_len = valid_len.numpy()
# detected 된 object들을 iteration 하면서 정보 추출. detect된 object의 갯수는 100개
for i in range(valid_len[0]):
# detection score를 iteration시 마다 높은 순으로 추출하고 SCORE_THRESHOLD보다 낮으면 loop 중단.
score = scores[0, i]
# detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
box = boxes[0, i]
''' **** 주의 ******
box는 ymin, xmin, ymax, xmax 순서로 되어 있음. 또한 원본 좌표값으로 되어 있음. '''
left = box[1]
top = box[0]
right = box[3]
bottom = box[2]
# class id 추출하고 class 명으로 매핑
class_id = classes[0, i]
caption = "{}: {:.4f}".format(labels_to_names[class_id], score)
print(caption)
#cv2.rectangle()은 인자로 들어온 draw_img에 사각형을 그림. 위치 인자는 반드시 정수형.
cv2.rectangle(draw_img, (int(left), int(top)), (int(right), int(bottom)), color=green_color, thickness=2)
cv2.putText(draw_img, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.4, red_color, 1)
if is_print:
print('Detection 수행시간:',round(time.time() - start_time, 2),"초")
return draw_img
import cv2
img_array = cv2.cvtColor(cv2.imread('/content/data/img01.png'), cv2.COLOR_BGR2RGB)
draw_img = get_detected_img(export_model, img_array, is_print=True)
plt.figure(figsize=(16, 16))
plt.imshow(draw_img)
car: 0.8458
car: 0.6979
car: 0.6889
car: 0.6842
car: 0.6794
car: 0.6492
car: 0.6140
motorcycle: 0.4841
motorcycle: 0.4483
car: 0.4398
bus: 0.4153
person: 0.4153
motorcycle: 0.4130
Detection 수행시간: 0.08 초
!wget -O ./data/beatles01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/beatles01.jpg
!wget -O ./data/baseball01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/baseball01.jpg
import cv2
img_array = cv2.cvtColor(cv2.imread('/content/data/beatles01.jpg'), cv2.COLOR_BGR2RGB)
print(img_array.shape)
draw_img = get_detected_img(export_model, img_array, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
(633, 806, 3)
/content/automl/efficientdet/utils.py:23: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
from tensorflow.python.tpu import tpu_function # pylint:disable=g-direct-tensorflow-import
/content/automl/efficientdet/utils.py:255: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
for u in self.updates:
person: 0.9486
person: 0.9406
person: 0.9362
person: 0.8914
car: 0.6025
car: 0.5251
Detection 수행시간: 4.61 초
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