강남역 남소관
우육도삭면
나는 육수를 좋아해서 기름진 육수는 괜찮았는데 도삭면 컨셉이라는 두꺼운 면은 좀 안맞더라...
볶음요리는 늘 맛있고
딤섬도 육즙 나오고 훌륭했다
| 더플라잉팬레드 강남역 (0) | 2021.12.05 |
|---|---|
| 딤딤섬 고속터미널 (0) | 2021.12.05 |
| 더현대 서울 로우엔슬로우 기네스 (0) | 2021.12.05 |
| 더현대 서울 호우섬 (0) | 2021.12.05 |
| 더현대 여의도 카멜커피 (0) | 2021.12.05 |
더현대 서울 로우엔슬로우 기네스
우연히 봤는데
기네스 맛도 끝내주고
버거도 한입한입이 맛있었고
무엇보다 과일샐러드가 정말 맛있었다
굿즈도 주고
희귀성도 있고
| 딤딤섬 고속터미널 (0) | 2021.12.05 |
|---|---|
| 강남역 남소관 (0) | 2021.12.05 |
| 더현대 서울 호우섬 (0) | 2021.12.05 |
| 더현대 여의도 카멜커피 (0) | 2021.12.05 |
| 강남역 마마스 (0) | 2021.12.05 |
더현대 서울 호우섬
와 만두 맛있다
소스도 맛있고
"역시 더현대다" 했다
| 강남역 남소관 (0) | 2021.12.05 |
|---|---|
| 더현대 서울 로우엔슬로우 기네스 (0) | 2021.12.05 |
| 더현대 여의도 카멜커피 (0) | 2021.12.05 |
| 강남역 마마스 (0) | 2021.12.05 |
| 이태원 두바이 (0) | 2021.11.30 |
더현대 서울 카멜커피
앙버터
라떼, 카멜커피
흠... 왜 인기있는지 모르겠다
카라멜마키아토풍 인테리어, 애매한 맛, 불편한 의자...
줄은 또 길고
| 더현대 서울 로우엔슬로우 기네스 (0) | 2021.12.05 |
|---|---|
| 더현대 서울 호우섬 (0) | 2021.12.05 |
| 강남역 마마스 (0) | 2021.12.05 |
| 이태원 두바이 (0) | 2021.11.30 |
| 압구정동 테스트키친 (0) | 2021.11.30 |
강남역 마마스
내가 무화과를 좋아해서... 먹었다.
생과일주스 맛있더라...
| 더현대 서울 호우섬 (0) | 2021.12.05 |
|---|---|
| 더현대 여의도 카멜커피 (0) | 2021.12.05 |
| 이태원 두바이 (0) | 2021.11.30 |
| 압구정동 테스트키친 (0) | 2021.11.30 |
| 더현대 디큐브 백미당 (2) | 2021.11.28 |
!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 |
+-----------------------------------------------------------------------------+
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 ModelInspectorMODEL = '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)
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)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: 7.936372518539429boxes
<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
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: 8.002159118652344labels_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
| 12-1. autoML efficientDet custom 데이터 train (0) | 2021.12.08 |
|---|---|
| 11-9. 이미지 크기 변경 후 efficient det inference (0) | 2021.12.08 |
| 11-4. tensorflow hub의 efficientDet lite 모델을 이용한 inference 실습 (0) | 2021.11.30 |
| 11-2~3. tensorflow hub의 efficientDet 모델을 이용한 inference 실습 (0) | 2021.11.30 |
| 11-1. AutoML EfficientDet 패키지 소개 (0) | 2021.11.25 |
import tensorflow as tf
import tensorflow_hub as hub
import cv2
import numpy as np
detector_automl_lite0 = hub.load("https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1")
img_array = cv2.cvtColor(cv2.imread('/content/data/baseball01.jpg'), cv2.COLOR_BGR2RGB)
#img_array_01 = img_array[np.newaxis, ...]
img_tensor = tf.convert_to_tensor(img_array, dtype=tf.uint8)[tf.newaxis, ...]
start_time = time.time()
# image를 detector_model에 인자로 입력하여 inference 수행.
#boxes, scores, classes, num_detections = detector_automl_lite0(img_array_01)
boxes, scores, classes, num_detections = detector_automl_lite0(img_tensor)
print('elapsed time:', time.time()-start_time)
# elapsed time: 2.2259414196014404
boxes.shape, scores.shape, classes.shape, num_detections
# (TensorShape([1, 100, 4]),
# TensorShape([1, 100]),
# TensorShape([1, 100]),
# <tf.Tensor: shape=(1,), dtype=int32, numpy=array([100], dtype=int32)>)
#좌표값이 0~1사이로 정규화 되지 않고 원본 이미지의 좌표값으로 반환
print('원본 이미지 shape:', img_array.shape)
boxes[0, 0:10], scores[0, :10], classes[0, :10]
원본 이미지 shape: (476, 735, 3)
(<tf.Tensor: shape=(10, 4), dtype=float32, numpy=
array([[202.11865 , 31.700203, 445.80273 , 188.72594 ],
[259.24213 , 174.63013 , 455.48645 , 373.06177 ],
[127.93706 , 324.82584 , 407.09464 , 493.36212 ],
[300.97815 , 331.1551 , 344.10986 , 374.19156 ],
[236.80632 , 542.42914 , 261.38422 , 609.6119 ],
[222.16089 , 659.98816 , 236.1999 , 678.62085 ],
[227.95496 , 496.07425 , 263.47733 , 610.33655 ],
[223.7558 , 647.2287 , 238.00728 , 661.4642 ],
[218.31972 , 654.4792 , 226.83658 , 662.96124 ],
[220.97037 , 494.3057 , 246.90009 , 588.9908 ]], dtype=float32)>,
<tf.Tensor: shape=(10,), dtype=float32, numpy=
array([0.90091765, 0.8834344 , 0.8455462 , 0.4854285 , 0.38026473,
0.2700993 , 0.26304004, 0.25670242, 0.17502265, 0.1738919 ],
dtype=float32)>,
<tf.Tensor: shape=(10,), dtype=float32, numpy=array([ 1., 1., 1., 40., 39., 37., 39., 37., 37., 39.], dtype=float32)>)
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_automl(model, img_array, score_threshold, object_show_count=100, 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, num_detections = model(img_tensor)
# Tensor값을 시각화를 위해 numpy 로 변환.
boxes = boxes.numpy()
scores = scores.numpy()
classes = classes.numpy()
num_detections = num_detections.numpy()
# detected 된 object들을 iteration 하면서 정보 추출. detect된 object의 갯수는 100개
for i in range(num_detections[0]):
# detection score를 iteration시 마다 높은 순으로 추출하고 SCORE_THRESHOLD보다 낮으면 loop 중단.
score = scores[0, i]
if score < score_threshold:
break
# 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
img_array = cv2.cvtColor(cv2.imread('/content/data/baseball01.jpg'), cv2.COLOR_BGR2RGB)
draw_img = get_detected_img_automl(detector_automl_lite0, img_array, score_threshold=0.3, object_show_count=100, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.9009
person: 0.8834
person: 0.8455
baseball glove: 0.4854
baseball bat: 0.3803
Detection 수행시간: 0.04 초
img_array = cv2.cvtColor(cv2.imread('/content/data/beatles01.jpg'), cv2.COLOR_BGR2RGB)
img_tensor = tf.convert_to_tensor(img_array, dtype=tf.uint8)[tf.newaxis, ...]
draw_img = get_detected_img_automl(detector_automl_lite0, img_array, score_threshold=0.3, object_show_count=100, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.8218
person: 0.8134
person: 0.7396
person: 0.6831
car: 0.6212
car: 0.4215
car: 0.3183
Detection 수행시간: 1.72 초
detector_automl_lite2 = hub.load("https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1")
img_array = cv2.cvtColor(cv2.imread('/content/data/beatles01.jpg'), cv2.COLOR_BGR2RGB)
img_tensor = tf.convert_to_tensor(img_array, dtype=tf.uint8)[tf.newaxis, ...]
draw_img = get_detected_img_automl(detector_automl_lite2, img_array, score_threshold=0.5, object_show_count=100, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.9152
person: 0.9089
person: 0.8914
person: 0.8808
car: 0.6071
car: 0.5114
Detection 수행시간: 2.73 초
def do_detected_video_automl(model, input_path, output_path, score_threshold, is_print):
cap = cv2.VideoCapture(input_path)
codec = cv2.VideoWriter_fourcc(*'XVID')
vid_size = (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
vid_fps = cap.get(cv2.CAP_PROP_FPS)
vid_writer = cv2.VideoWriter(output_path, codec, vid_fps, vid_size)
frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print('총 Frame 갯수:', frame_cnt)
green_color=(0, 255, 0)
red_color=(0, 0, 255)
while True:
hasFrame, img_frame = cap.read()
if not hasFrame:
print('더 이상 처리할 frame이 없습니다.')
break
# get_detected_img_automl() 호출
img_frame = get_detected_img_automl(model, img_frame, score_threshold=score_threshold, object_show_count=100, is_print=is_print)
vid_writer.write(img_frame)
# end of while loop
vid_writer.release()
cap.release()
do_detected_video_automl(detector_automl_lite2, '/content/data/Jonh_Wick_small.mp4', './data/John_Wick_small_lite_02.mp4', 0.5, True)
총 Frame 갯수: 58
car: 0.7213
car: 0.7048
person: 0.6839
car: 0.6667
car: 0.6558
car: 0.5763
Detection 수행시간: 2.18 초
car: 0.7208
car: 0.7070
person: 0.6858
car: 0.6600
car: 0.6558
car: 0.5790
Detection 수행시간: 0.07 초
car: 0.6072
person: 0.5958
car: 0.5877
car: 0.5727
Detection 수행시간: 0.06 초
car: 0.6773
person: 0.6331
car: 0.5557
Detection 수행시간: 0.06 초
car: 0.7090
person: 0.6330
car: 0.6061
car: 0.5067
Detection 수행시간: 0.06 초
car: 0.6947
car: 0.6896
person: 0.5417
Detection 수행시간: 0.06 초
car: 0.6893
car: 0.6833
person: 0.5335
Detection 수행시간: 0.06 초
car: 0.7336
car: 0.7109
car: 0.6813
motorcycle: 0.5120
Detection 수행시간: 0.06 초
car: 0.7302
car: 0.7150
motorcycle: 0.6327
car: 0.6062
Detection 수행시간: 0.06 초
car: 0.7211
car: 0.7191
motorcycle: 0.6324
car: 0.6052
Detection 수행시간: 0.06 초
car: 0.7077
car: 0.6678
car: 0.6344
motorcycle: 0.6321
Detection 수행시간: 0.06 초
car: 0.7145
car: 0.7095
car: 0.6083
motorcycle: 0.6038
Detection 수행시간: 0.06 초
car: 0.7381
car: 0.6946
motorcycle: 0.5520
car: 0.5499
Detection 수행시간: 0.06 초
car: 0.7156
car: 0.6753
car: 0.6429
motorcycle: 0.5482
Detection 수행시간: 0.06 초
car: 0.7285
car: 0.6851
car: 0.6462
motorcycle: 0.5571
Detection 수행시간: 0.06 초
car: 0.7230
car: 0.6673
person: 0.6127
car: 0.5793
motorcycle: 0.5393
Detection 수행시간: 0.06 초
car: 0.7441
car: 0.6925
motorcycle: 0.5914
person: 0.5100
Detection 수행시간: 0.07 초
car: 0.6714
car: 0.6340
person: 0.5797
Detection 수행시간: 0.06 초
car: 0.7087
motorcycle: 0.6122
car: 0.6025
car: 0.5611
car: 0.5445
Detection 수행시간: 0.06 초
car: 0.7057
motorcycle: 0.6146
car: 0.5860
car: 0.5615
car: 0.5392
Detection 수행시간: 0.06 초
car: 0.7421
motorcycle: 0.6152
car: 0.5331
car: 0.5200
car: 0.5190
Detection 수행시간: 0.06 초
car: 0.7229
motorcycle: 0.6333
car: 0.5775
car: 0.5757
Detection 수행시간: 0.06 초
car: 0.6589
car: 0.5912
motorcycle: 0.5899
Detection 수행시간: 0.06 초
person: 0.7521
person: 0.6622
motorcycle: 0.5707
Detection 수행시간: 0.06 초
person: 0.7558
person: 0.6703
motorcycle: 0.5853
Detection 수행시간: 0.06 초
person: 0.7575
person: 0.7115
Detection 수행시간: 0.06 초
person: 0.7646
person: 0.7391
Detection 수행시간: 0.06 초
person: 0.7744
person: 0.7480
Detection 수행시간: 0.06 초
person: 0.7778
car: 0.7730
horse: 0.7068
car: 0.5107
Detection 수행시간: 0.06 초
car: 0.7756
person: 0.7676
horse: 0.7113
car: 0.5043
Detection 수행시간: 0.06 초
car: 0.7500
person: 0.6981
person: 0.6900
car: 0.5125
Detection 수행시간: 0.06 초
car: 0.6990
person: 0.6905
person: 0.6854
Detection 수행시간: 0.06 초
person: 0.7262
car: 0.7237
person: 0.6961
motorcycle: 0.5003
Detection 수행시간: 0.06 초
car: 0.7820
person: 0.7677
person: 0.6664
Detection 수행시간: 0.06 초
car: 0.7785
person: 0.7709
person: 0.6833
Detection 수행시간: 0.06 초
car: 0.8247
person: 0.7472
person: 0.7321
car: 0.6222
motorcycle: 0.5294
Detection 수행시간: 0.06 초
car: 0.8342
person: 0.7343
person: 0.7152
car: 0.5990
motorcycle: 0.5350
Detection 수행시간: 0.06 초
car: 0.8224
person: 0.7061
person: 0.7014
motorcycle: 0.5112
Detection 수행시간: 0.06 초
car: 0.8180
person: 0.7044
person: 0.6811
car: 0.5980
Detection 수행시간: 0.06 초
car: 0.8190
person: 0.7017
person: 0.6996
car: 0.6456
motorcycle: 0.5053
Detection 수행시간: 0.06 초
car: 0.8029
person: 0.7463
person: 0.6732
car: 0.6710
Detection 수행시간: 0.06 초
car: 0.7811
person: 0.7525
person: 0.7167
car: 0.7015
Detection 수행시간: 0.06 초
car: 0.7778
person: 0.7342
car: 0.6729
person: 0.6687
motorcycle: 0.5495
Detection 수행시간: 0.06 초
car: 0.8041
person: 0.7691
horse: 0.6278
car: 0.5558
Detection 수행시간: 0.06 초
car: 0.8021
person: 0.7739
horse: 0.6354
car: 0.5613
Detection 수행시간: 0.06 초
car: 0.8237
person: 0.7113
person: 0.6251
horse: 0.5672
car: 0.5561
motorcycle: 0.5362
Detection 수행시간: 0.06 초
car: 0.8573
car: 0.6713
person: 0.5939
person: 0.5814
car: 0.5653
Detection 수행시간: 0.06 초
car: 0.8395
car: 0.7330
person: 0.5949
person: 0.5404
horse: 0.5206
Detection 수행시간: 0.06 초
car: 0.8135
horse: 0.6740
car: 0.6538
person: 0.6302
person: 0.5339
Detection 수행시간: 0.06 초
car: 0.8160
horse: 0.6635
car: 0.6524
person: 0.6147
person: 0.5334
Detection 수행시간: 0.06 초
car: 0.8571
person: 0.6551
car: 0.6094
horse: 0.5733
person: 0.5346
Detection 수행시간: 0.06 초
car: 0.8386
car: 0.6963
horse: 0.6246
person: 0.5747
person: 0.5439
Detection 수행시간: 0.07 초
car: 0.8146
car: 0.6012
person: 0.5879
horse: 0.5211
Detection 수행시간: 0.06 초
car: 0.7695
car: 0.7263
person: 0.5264
horse: 0.5262
motorcycle: 0.5096
Detection 수행시간: 0.06 초
car: 0.7618
car: 0.7197
person: 0.5401
horse: 0.5215
motorcycle: 0.5116
Detection 수행시간: 0.06 초
car: 0.7638
car: 0.7306
person: 0.6056
car: 0.5946
person: 0.5777
Detection 수행시간: 0.07 초
car: 0.7433
car: 0.6687
car: 0.6672
person: 0.5707
person: 0.5527
Detection 수행시간: 0.06 초
car: 0.7775
car: 0.7218
car: 0.6586
person: 0.5992
Detection 수행시간: 0.06 초
더 이상 처리할 frame이 없습니다.
| 11-9. 이미지 크기 변경 후 efficient det inference (0) | 2021.12.08 |
|---|---|
| 11-6~8. automl efficientDet (0) | 2021.11.30 |
| 11-2~3. tensorflow hub의 efficientDet 모델을 이용한 inference 실습 (0) | 2021.11.30 |
| 11-1. AutoML EfficientDet 패키지 소개 (0) | 2021.11.25 |
| 10-7. Compound Scaling (0) | 2021.10.29 |
import tensorflow as tf
#tensorflow_hub import 수행.
import tensorflow_hub as hub
import matplotlib.pyplot as plt
print(tf.__version__)
# 2.7.0
!nvidia-smi
Sat Dec 25 03:17:11 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 71C P8 34W / 149W | 0MiB / 11441MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
!mkdir /content/data
!wget -O ./data/beatles01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/beatles01.jpg
module_handle = "https://tfhub.dev/tensorflow/efficientdet/d0/1"
detector_model = hub.load(module_handle)
WARNING:absl:Importing a function (__inference___call___32344) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_97451) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_77595) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_103456) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_93843) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_107064) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_75975) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
import cv2
import time
import numpy as np
img_array_np = cv2.imread('/content/data/beatles01.jpg')
img_array = img_array_np[np.newaxis, ...]
print(img_array_np.shape, img_array.shape)
start_time = time.time()
# image를 detector_model에 인자로 입력하여 inference 수행.
result = detector_model(img_array)
print('elapsed time:', time.time()-start_time)
# (633, 806, 3) (1, 633, 806, 3)
# elapsed time: 13.12512469291687
img_tensor = tf.convert_to_tensor(img_array_np, dtype=tf.uint8)[tf.newaxis, ...]
start_time = time.time()
# image를 detector_model에 인자로 입력하여 inference 수행.
result = detector_model(img_tensor)
print('elapsed time:', time.time()-start_time)
# elapsed time: 0.3127138614654541
# image를 numpy가 아니라 tensor로 로딩
def load_img(path):
img = tf.io.read_file(path)
#png 파일일 경우 decode_png()호출
img = tf.image.decode_jpeg(img, channels=3)
print(img.shape, type(img))
return img
import time
# image를 tensor형태로 로딩.
img = load_img('/content/data/beatles01.jpg')
# 3차원 image tensor를 4차원 tensor로 변환.
# Efficientdet d0의 경우에는 입력 image를 unit8로 적용 필요.
converted_img = tf.image.convert_image_dtype(img, tf.uint8)[tf.newaxis, ...]
start_time = time.time()
# image를 detector_model에 인자로 입력하여 inference 수행.
result = detector_model(converted_img)
print('elapsed time:', time.time()-start_time)
# (633, 806, 3) <class 'tensorflow.python.framework.ops.EagerTensor'>
# elapsed time: 0.3045518398284912
# inference 결과 출력. dictionary 형태의 출력 결과. dict내부의 key는 model 별로 서로 다름. 출력하여 key값 확인 필요.
print(result)
{'detection_classes': <tf.Tensor: shape=(1, 100), dtype=float32, numpy=
array([[ 1., 1., 1., 1., 3., 3., 8., 41., 3., 3., 3., 3., 3.,
41., 8., 3., 3., 3., 3., 31., 31., 3., 3., 3., 3., 3.,
3., 3., 31., 3., 3., 3., 3., 1., 3., 10., 3., 3., 3.,
31., 15., 32., 10., 3., 10., 1., 10., 3., 3., 3., 31., 1.,
27., 10., 33., 8., 3., 1., 31., 10., 3., 8., 3., 1., 3.,
1., 3., 3., 3., 3., 3., 32., 3., 1., 1., 31., 3., 31.,
1., 3., 31., 10., 8., 3., 3., 32., 1., 1., 3., 10., 41.,
31., 1., 31., 10., 3., 3., 32., 31., 3.]], dtype=float32)>, 'raw_detection_scores': <tf.Tensor: shape=(1, 49104, 90), dtype=float32, numpy=
array([[[1.4354929e-01, 3.5354502e-02, 1.3373634e-01, ...,
3.8939363e-03, 4.5267404e-03, 7.4508404e-03],
[5.8584111e-03, 1.5053916e-03, 5.7255975e-03, ...,
2.8605445e-04, 7.9238508e-04, 5.8379129e-04],
[3.8943693e-04, 1.2312026e-04, 2.9878854e-04, ...,
1.8558223e-05, 2.8921681e-04, 6.1487677e-05],
...,
[1.9985980e-03, 9.0183894e-04, 1.1327897e-03, ...,
6.9412554e-04, 9.2282181e-04, 1.1239841e-03],
[2.4827726e-03, 1.0236464e-03, 1.1463074e-03, ...,
8.3448330e-04, 9.3340850e-04, 9.7718462e-04],
[3.6730708e-03, 1.0787870e-03, 1.3733003e-03, ...,
1.0809251e-03, 9.1082731e-04, 1.2321979e-03]]], dtype=float32)>, 'raw_detection_boxes': <tf.Tensor: shape=(1, 49104, 4), dtype=float32, numpy=
array([[[ 2.9779205e-01, 9.7853315e-01, 4.0974966e-01, 9.7853315e-01],
[ 4.0937748e-02, 7.5187005e-02, 6.7204766e-02, 7.7955268e-02],
[-1.1543997e-02, 1.3033487e-04, 7.8647465e-02, 5.4135054e-02],
...,
[ 3.0034673e-01, 5.5941677e-01, 1.4586637e+00, 1.1626569e+00],
[ 1.6176426e-01, 4.5902258e-01, 1.6337620e+00, 1.2052057e+00],
[-1.5788192e-01, 2.7244717e-01, 1.8246810e+00, 1.3288105e+00]]],
dtype=float32)>, 'detection_anchor_indices': <tf.Tensor: shape=(1, 100), dtype=float32, numpy=
array([[47257., 47320., 47347., 47284., 40095., 12485., 11912., 23426.,
11178., 10677., 11280., 11871., 10632., 22826., 12800., 10686.,
10623., 10056., 10047., 22085., 18745., 9498., 10074., 10065.,
11271., 11880., 9489., 10101., 41415., 11262., 10614., 10668.,
10083., 13020., 10092., 9462., 10038., 9480., 10641., 18304.,
42941., 16369., 9498., 10695., 9453., 10569., 9489., 9471.,
9462., 12875., 41128., 11166., 42426., 9471., 42426., 11178.,
11145., 9462., 18764., 9480., 10569., 11295., 10605., 47297.,
10110., 11157., 10704., 10119., 10683., 10713., 10659., 15783.,
12888., 13029., 10011., 41417., 10128., 22060., 9453., 9507.,
41352., 8877., 11880., 11856., 10650., 17529., 11958., 10047.,
12330., 7824., 42823., 41697., 11967., 17016., 8886., 12327.,
9453., 17520., 22051., 10578.]], dtype=float32)>, 'detection_scores': <tf.Tensor: shape=(1, 100), dtype=float32, numpy=
array([[0.94711405, 0.935974 , 0.930035 , 0.89913994, 0.625541 ,
0.48422325, 0.3482024 , 0.31519073, 0.31252164, 0.3096477 ,
0.2892261 , 0.26785725, 0.26200417, 0.2544667 , 0.24920423,
0.24709295, 0.22555462, 0.22262326, 0.20144816, 0.1989274 ,
0.19818683, 0.19184774, 0.18933022, 0.18806441, 0.17253922,
0.16980891, 0.16840717, 0.16792467, 0.16733843, 0.16649997,
0.16592245, 0.16406913, 0.15594032, 0.14497948, 0.144825 ,
0.14451697, 0.14210103, 0.13976108, 0.13904284, 0.1389742 ,
0.13756527, 0.13691278, 0.13502952, 0.13211057, 0.13042402,
0.12915237, 0.12603028, 0.124609 , 0.12447888, 0.12250288,
0.12192409, 0.12113374, 0.12100718, 0.11963245, 0.11917206,
0.11773309, 0.11646152, 0.11581548, 0.11349872, 0.11340918,
0.11095154, 0.1098927 , 0.10858452, 0.10815845, 0.10694698,
0.10670073, 0.10592487, 0.1055002 , 0.10536031, 0.1041959 ,
0.10418969, 0.10327245, 0.10291874, 0.10186002, 0.1013522 ,
0.10121182, 0.10055887, 0.09975425, 0.09954076, 0.09945919,
0.09853141, 0.09829449, 0.09807272, 0.09666188, 0.09628956,
0.09607641, 0.09603674, 0.09379527, 0.09334304, 0.09319282,
0.09244616, 0.09126819, 0.09113833, 0.09095921, 0.09013715,
0.08883683, 0.08855603, 0.08836359, 0.0877557 , 0.08759023]],
dtype=float32)>, 'detection_multiclass_scores': <tf.Tensor: shape=(1, 100, 90), dtype=float32, numpy=
array([[[9.4711405e-01, 8.6817035e-04, 3.0984138e-03, ...,
4.7520236e-03, 5.2246830e-04, 4.5701285e-04],
[9.3597400e-01, 6.4510002e-04, 2.2451645e-03, ...,
3.9743381e-03, 8.1474200e-04, 5.4064585e-04],
[9.3003500e-01, 1.2423380e-03, 4.1281143e-03, ...,
4.1296966e-03, 1.3249756e-03, 8.3464832e-04],
...,
[1.8119732e-02, 5.0535421e-03, 1.0724876e-03, ...,
1.3032763e-02, 8.9723011e-03, 1.0205678e-02],
[1.1448574e-02, 1.6255280e-02, 1.1670285e-02, ...,
1.7721199e-03, 5.5407584e-03, 2.2403533e-03],
[8.4233090e-02, 1.9779259e-02, 8.7590225e-02, ...,
9.6069882e-04, 1.3168838e-03, 1.2544082e-03]]], dtype=float32)>, 'num_detections': <tf.Tensor: shape=(1,), dtype=float32, numpy=array([100.], dtype=float32)>, 'detection_boxes': <tf.Tensor: shape=(1, 100, 4), dtype=float32, numpy=
array([[[4.1179040e-01, 6.3390382e-02, 8.8111836e-01, 2.6741692e-01],
[4.3304449e-01, 4.7706339e-01, 8.9250046e-01, 6.8628871e-01],
[4.1960636e-01, 6.8270773e-01, 8.9688581e-01, 8.9498097e-01],
[4.1164526e-01, 2.6410934e-01, 8.6583102e-01, 4.6428421e-01],
[3.8654572e-01, 1.7934969e-01, 5.4316032e-01, 3.2028845e-01],
[3.6050096e-01, 6.2638736e-01, 4.6446508e-01, 7.1950281e-01],
[3.5996163e-01, 6.2458235e-01, 4.6350214e-01, 7.2000319e-01],
[7.1983784e-01, 6.2759423e-01, 8.6870378e-01, 7.0305586e-01],
[3.6646506e-01, 3.8801342e-01, 4.2244112e-01, 4.3741155e-01],
[3.5229647e-01, 5.4654634e-01, 3.8833630e-01, 5.7694817e-01],
[3.6912179e-01, 5.8222091e-01, 4.1025555e-01, 6.2288415e-01],
[3.7102419e-01, 5.9425962e-01, 4.3720052e-01, 6.3780034e-01],
[3.5444006e-01, 4.7450614e-01, 3.8303307e-01, 4.9580967e-01],
[7.1531910e-01, 5.7743710e-01, 8.7339032e-01, 6.8964785e-01],
[3.8982463e-01, 1.7769308e-01, 5.3870612e-01, 3.1275189e-01],
[3.5579172e-01, 5.5832678e-01, 3.9027628e-01, 5.8628565e-01],
[3.5591349e-01, 4.6058959e-01, 3.8360608e-01, 4.8057848e-01],
[3.4581965e-01, 4.7958860e-01, 3.6583403e-01, 4.9762627e-01],
[3.4433401e-01, 4.6549153e-01, 3.6546969e-01, 4.8306775e-01],
[6.8952668e-01, 2.8691068e-01, 8.4429270e-01, 3.8039652e-01],
[5.6328285e-01, 5.2353203e-01, 7.0950598e-01, 5.6712413e-01],
[3.2798409e-01, 5.0720298e-01, 3.4583938e-01, 5.2633405e-01],
[3.4163418e-01, 5.0696808e-01, 3.6424884e-01, 5.2613205e-01],
[3.4342399e-01, 4.9362123e-01, 3.6436176e-01, 5.1271427e-01],
[3.6820206e-01, 5.7783151e-01, 4.0318635e-01, 6.0513425e-01],
[3.7084836e-01, 6.0645926e-01, 4.4600427e-01, 6.5543246e-01],
[3.3108306e-01, 4.9567217e-01, 3.4666312e-01, 5.1192003e-01],
[3.4755635e-01, 5.5521488e-01, 3.6801580e-01, 5.7478154e-01],
[5.0978678e-01, 7.5387210e-01, 6.8267655e-01, 8.3977443e-01],
[3.6682692e-01, 5.6671417e-01, 3.9800602e-01, 5.9056568e-01],
[3.5695317e-01, 4.3974501e-01, 3.8740516e-01, 4.6231419e-01],
[3.5100028e-01, 5.3704566e-01, 3.8353223e-01, 5.6229430e-01],
[3.4340435e-01, 5.2385479e-01, 3.6579999e-01, 5.4249030e-01],
[4.2547816e-01, 5.8302408e-01, 5.0048357e-01, 6.2211710e-01],
[3.4608695e-01, 5.3925014e-01, 3.6758476e-01, 5.5907607e-01],
[3.2927078e-01, 4.5257777e-01, 3.4853238e-01, 4.6549708e-01],
[3.4211871e-01, 4.5176423e-01, 3.6421466e-01, 4.6783006e-01],
[3.3232009e-01, 4.8148558e-01, 3.4683454e-01, 4.9636772e-01],
[3.5374671e-01, 4.8738575e-01, 3.8187450e-01, 5.0869471e-01],
[5.4104501e-01, 7.4378175e-01, 6.8199605e-01, 8.0373544e-01],
[7.4930298e-01, 6.7912787e-04, 9.9816215e-01, 2.2687393e-01],
[4.8811194e-01, 3.8788208e-01, 6.5886742e-01, 4.4012472e-01],
[3.2798409e-01, 5.0720298e-01, 3.4583938e-01, 5.2633405e-01],
[3.6192855e-01, 5.7177728e-01, 3.8971961e-01, 5.9754187e-01],
[3.3015379e-01, 4.4123974e-01, 3.4820625e-01, 4.5039704e-01],
[3.5280249e-01, 3.7495017e-01, 3.9356515e-01, 3.8802660e-01],
[3.3108306e-01, 4.9567217e-01, 3.4666312e-01, 5.1192003e-01],
[3.3070818e-01, 4.6405959e-01, 3.4772059e-01, 4.7952139e-01],
[3.2927078e-01, 4.5257777e-01, 3.4853238e-01, 4.6549708e-01],
[3.9903688e-01, 2.8748375e-01, 4.9881092e-01, 4.1517448e-01],
[4.5144099e-01, 7.3607433e-01, 7.1501023e-01, 8.5484862e-01],
[3.5879132e-01, 3.7910637e-01, 4.1088080e-01, 3.9561763e-01],
[6.9514066e-01, 2.8370458e-01, 8.4553832e-01, 3.8235956e-01],
[3.3070818e-01, 4.6405959e-01, 3.4772059e-01, 4.7952139e-01],
[6.9514066e-01, 2.8370458e-01, 8.4553832e-01, 3.8235956e-01],
[3.6646506e-01, 3.8801342e-01, 4.2244112e-01, 4.3741155e-01],
[3.7016463e-01, 3.7109971e-01, 4.0168965e-01, 3.8679004e-01],
[3.2927078e-01, 4.5257777e-01, 3.4853238e-01, 4.6549708e-01],
[5.4758275e-01, 5.2163035e-01, 7.1873194e-01, 5.9703213e-01],
[3.3232009e-01, 4.8148558e-01, 3.4683454e-01, 4.9636772e-01],
[3.5280249e-01, 3.7495017e-01, 3.9356515e-01, 3.8802660e-01],
[3.6760172e-01, 5.8778751e-01, 4.2486581e-01, 6.3663971e-01],
[3.6506388e-01, 4.2663202e-01, 3.9124954e-01, 4.4209841e-01],
[4.1215658e-01, 3.4202617e-01, 8.8318264e-01, 6.1285341e-01],
[3.4818730e-01, 5.6928444e-01, 3.6841545e-01, 5.8634388e-01],
[3.5158402e-01, 3.6752591e-01, 3.9787045e-01, 3.8494465e-01],
[3.6561641e-01, 5.8981210e-01, 3.8907713e-01, 6.1613721e-01],
[3.4828454e-01, 5.8140194e-01, 3.6721477e-01, 5.9566200e-01],
[3.4462604e-01, 5.3514403e-01, 3.9644474e-01, 5.8370894e-01],
[3.6365288e-01, 5.9567219e-01, 3.9053750e-01, 6.3109928e-01],
[3.5137120e-01, 5.2337110e-01, 3.7918985e-01, 5.4226983e-01],
[4.9033237e-01, 3.8423172e-01, 6.1302155e-01, 4.1986457e-01],
[4.1236705e-01, 3.5832933e-01, 4.8268488e-01, 4.1150209e-01],
[4.1843241e-01, 6.0260159e-01, 4.9375936e-01, 6.3197130e-01],
[3.4338030e-01, 4.0502068e-01, 3.6552405e-01, 4.1651794e-01],
[4.2186460e-01, 7.0060635e-01, 8.9403832e-01, 8.9042270e-01],
[3.4787107e-01, 5.9927994e-01, 3.6625677e-01, 6.1748618e-01],
[7.1091282e-01, 2.7190217e-01, 8.2108068e-01, 3.5610101e-01],
[3.3015379e-01, 4.4123974e-01, 3.4820625e-01, 4.5039704e-01],
[3.2861453e-01, 5.2156895e-01, 3.4669027e-01, 5.3986579e-01],
[4.9491951e-01, 5.3556001e-01, 7.1317983e-01, 6.2238050e-01],
[3.1718564e-01, 4.4188350e-01, 3.3180881e-01, 4.5035261e-01],
[3.7084836e-01, 6.0645926e-01, 4.4600427e-01, 6.5543246e-01],
[3.8386708e-01, 5.8912140e-01, 4.2566502e-01, 6.2365121e-01],
[3.5371971e-01, 5.0196201e-01, 3.8005614e-01, 5.2139860e-01],
[5.9429705e-01, 4.1528758e-01, 6.5035301e-01, 4.4080022e-01],
[3.6265263e-01, 7.5203842e-01, 4.6041501e-01, 7.7500635e-01],
[3.4433401e-01, 4.6549153e-01, 3.6546969e-01, 4.8306775e-01],
[3.8074300e-01, 3.8487837e-01, 4.5196366e-01, 4.3506762e-01],
[2.5742817e-01, 6.0831654e-01, 2.8681454e-01, 6.2162197e-01],
[7.1172494e-01, 6.1941981e-01, 8.8754946e-01, 7.4794185e-01],
[5.6854314e-01, 7.4925357e-01, 7.2917712e-01, 8.5470790e-01],
[3.6275440e-01, 7.5717753e-01, 4.6149722e-01, 7.8934973e-01],
[5.5307239e-01, 5.2523065e-01, 6.6746283e-01, 5.5445266e-01],
[3.1785089e-01, 4.5512110e-01, 3.3233309e-01, 4.6439195e-01],
[4.1034609e-01, 3.8362762e-01, 4.7151357e-01, 4.1939363e-01],
[3.3015379e-01, 4.4123974e-01, 3.4820625e-01, 4.5039704e-01],
[5.6086457e-01, 4.0125006e-01, 6.4354509e-01, 4.3456870e-01],
[7.1788090e-01, 2.4195449e-01, 8.1452870e-01, 3.1889099e-01],
[3.5509771e-01, 3.8778654e-01, 3.9057735e-01, 3.9996943e-01]]],
dtype=float32)>}
print(result.keys())
# detect 결과는 100개를 기본으로 Detect 함(즉 Detect된 오브젝트는 무조건 100개. 그래서 tensor(array)는 100개 단위, num_detections는 100)
print(result['detection_boxes'].shape, result['detection_classes'].shape, result['detection_scores'].shape, result['num_detections'])
# dict_keys(['detection_classes', 'raw_detection_scores', 'raw_detection_boxes', 'detection_anchor_indices', 'detection_scores', 'detection_multiclass_scores', 'num_detections', 'detection_boxes'])
# (1, 100, 4) (1, 100) (1, 100) tf.Tensor([100.], shape=(1,), dtype=float32)
# detect된 object들은 detection score가 높은 순으로 array값을 순차적으로 채움.
print('#### detection_classes #####')
print(result['detection_classes'])
print('#### detection_scores #####')
print(result['detection_scores'])
#### detection_classes #####
tf.Tensor(
[[ 1. 1. 1. 1. 3. 3. 8. 41. 3. 3. 3. 3. 3. 41. 8. 3. 3. 3.
3. 31. 31. 3. 3. 3. 3. 3. 3. 3. 31. 3. 3. 3. 3. 1. 3. 10.
3. 3. 3. 31. 15. 32. 10. 3. 10. 1. 10. 3. 3. 3. 31. 1. 27. 10.
33. 8. 3. 1. 31. 10. 3. 8. 3. 1. 3. 1. 3. 3. 3. 3. 3. 32.
3. 1. 1. 31. 3. 31. 1. 3. 31. 10. 8. 3. 3. 32. 1. 1. 3. 10.
41. 31. 1. 31. 10. 3. 3. 32. 31. 3.]], shape=(1, 100), dtype=float32)
#### detection_scores #####
tf.Tensor(
[[0.94711405 0.935974 0.930035 0.89913994 0.625541 0.48422325
0.3482024 0.31519073 0.31252164 0.3096477 0.2892261 0.26785725
0.26200417 0.2544667 0.24920423 0.24709295 0.22555462 0.22262326
0.20144816 0.1989274 0.19818683 0.19184774 0.18933022 0.18806441
0.17253922 0.16980891 0.16840717 0.16792467 0.16733843 0.16649997
0.16592245 0.16406913 0.15594032 0.14497948 0.144825 0.14451697
0.14210103 0.13976108 0.13904284 0.1389742 0.13756527 0.13691278
0.13502952 0.13211057 0.13042402 0.12915237 0.12603028 0.124609
0.12447888 0.12250288 0.12192409 0.12113374 0.12100718 0.11963245
0.11917206 0.11773309 0.11646152 0.11581548 0.11349872 0.11340918
0.11095154 0.1098927 0.10858452 0.10815845 0.10694698 0.10670073
0.10592487 0.1055002 0.10536031 0.1041959 0.10418969 0.10327245
0.10291874 0.10186002 0.1013522 0.10121182 0.10055887 0.09975425
0.09954076 0.09945919 0.09853141 0.09829449 0.09807272 0.09666188
0.09628956 0.09607641 0.09603674 0.09379527 0.09334304 0.09319282
0.09244616 0.09126819 0.09113833 0.09095921 0.09013715 0.08883683
0.08855603 0.08836359 0.0877557 0.08759023]], shape=(1, 100), dtype=float32)
# bounding box 좌표는 ymin, xmin, ymax, xmax 순서로 반환됨. y가 먼저, x가 나중에 나오므로 반드시 주의해야 함.
# 좌표 값은 원본 이미지의 width, height로 0~1 사이값으로 정규화됨.
print('#### detection_boxes #####')
print(result['detection_boxes'])
#### detection_boxes #####
tf.Tensor(
[[[4.1179040e-01 6.3390382e-02 8.8111836e-01 2.6741692e-01]
[4.3304449e-01 4.7706339e-01 8.9250046e-01 6.8628871e-01]
[4.1960636e-01 6.8270773e-01 8.9688581e-01 8.9498097e-01]
[4.1164526e-01 2.6410934e-01 8.6583102e-01 4.6428421e-01]
[3.8654572e-01 1.7934969e-01 5.4316032e-01 3.2028845e-01]
[3.6050096e-01 6.2638736e-01 4.6446508e-01 7.1950281e-01]
[3.5996163e-01 6.2458235e-01 4.6350214e-01 7.2000319e-01]
[7.1983784e-01 6.2759423e-01 8.6870378e-01 7.0305586e-01]
[3.6646506e-01 3.8801342e-01 4.2244112e-01 4.3741155e-01]
[3.5229647e-01 5.4654634e-01 3.8833630e-01 5.7694817e-01]
[3.6912179e-01 5.8222091e-01 4.1025555e-01 6.2288415e-01]
[3.7102419e-01 5.9425962e-01 4.3720052e-01 6.3780034e-01]
[3.5444006e-01 4.7450614e-01 3.8303307e-01 4.9580967e-01]
[7.1531910e-01 5.7743710e-01 8.7339032e-01 6.8964785e-01]
[3.8982463e-01 1.7769308e-01 5.3870612e-01 3.1275189e-01]
[3.5579172e-01 5.5832678e-01 3.9027628e-01 5.8628565e-01]
[3.5591349e-01 4.6058959e-01 3.8360608e-01 4.8057848e-01]
[3.4581965e-01 4.7958860e-01 3.6583403e-01 4.9762627e-01]
[3.4433401e-01 4.6549153e-01 3.6546969e-01 4.8306775e-01]
[6.8952668e-01 2.8691068e-01 8.4429270e-01 3.8039652e-01]
[5.6328285e-01 5.2353203e-01 7.0950598e-01 5.6712413e-01]
[3.2798409e-01 5.0720298e-01 3.4583938e-01 5.2633405e-01]
[3.4163418e-01 5.0696808e-01 3.6424884e-01 5.2613205e-01]
[3.4342399e-01 4.9362123e-01 3.6436176e-01 5.1271427e-01]
[3.6820206e-01 5.7783151e-01 4.0318635e-01 6.0513425e-01]
[3.7084836e-01 6.0645926e-01 4.4600427e-01 6.5543246e-01]
[3.3108306e-01 4.9567217e-01 3.4666312e-01 5.1192003e-01]
[3.4755635e-01 5.5521488e-01 3.6801580e-01 5.7478154e-01]
[5.0978678e-01 7.5387210e-01 6.8267655e-01 8.3977443e-01]
[3.6682692e-01 5.6671417e-01 3.9800602e-01 5.9056568e-01]
[3.5695317e-01 4.3974501e-01 3.8740516e-01 4.6231419e-01]
[3.5100028e-01 5.3704566e-01 3.8353223e-01 5.6229430e-01]
[3.4340435e-01 5.2385479e-01 3.6579999e-01 5.4249030e-01]
[4.2547816e-01 5.8302408e-01 5.0048357e-01 6.2211710e-01]
[3.4608695e-01 5.3925014e-01 3.6758476e-01 5.5907607e-01]
[3.2927078e-01 4.5257777e-01 3.4853238e-01 4.6549708e-01]
[3.4211871e-01 4.5176423e-01 3.6421466e-01 4.6783006e-01]
[3.3232009e-01 4.8148558e-01 3.4683454e-01 4.9636772e-01]
[3.5374671e-01 4.8738575e-01 3.8187450e-01 5.0869471e-01]
[5.4104501e-01 7.4378175e-01 6.8199605e-01 8.0373544e-01]
[7.4930298e-01 6.7912787e-04 9.9816215e-01 2.2687393e-01]
[4.8811194e-01 3.8788208e-01 6.5886742e-01 4.4012472e-01]
[3.2798409e-01 5.0720298e-01 3.4583938e-01 5.2633405e-01]
[3.6192855e-01 5.7177728e-01 3.8971961e-01 5.9754187e-01]
[3.3015379e-01 4.4123974e-01 3.4820625e-01 4.5039704e-01]
[3.5280249e-01 3.7495017e-01 3.9356515e-01 3.8802660e-01]
[3.3108306e-01 4.9567217e-01 3.4666312e-01 5.1192003e-01]
[3.3070818e-01 4.6405959e-01 3.4772059e-01 4.7952139e-01]
[3.2927078e-01 4.5257777e-01 3.4853238e-01 4.6549708e-01]
[3.9903688e-01 2.8748375e-01 4.9881092e-01 4.1517448e-01]
[4.5144099e-01 7.3607433e-01 7.1501023e-01 8.5484862e-01]
[3.5879132e-01 3.7910637e-01 4.1088080e-01 3.9561763e-01]
[6.9514066e-01 2.8370458e-01 8.4553832e-01 3.8235956e-01]
[3.3070818e-01 4.6405959e-01 3.4772059e-01 4.7952139e-01]
[6.9514066e-01 2.8370458e-01 8.4553832e-01 3.8235956e-01]
[3.6646506e-01 3.8801342e-01 4.2244112e-01 4.3741155e-01]
[3.7016463e-01 3.7109971e-01 4.0168965e-01 3.8679004e-01]
[3.2927078e-01 4.5257777e-01 3.4853238e-01 4.6549708e-01]
[5.4758275e-01 5.2163035e-01 7.1873194e-01 5.9703213e-01]
[3.3232009e-01 4.8148558e-01 3.4683454e-01 4.9636772e-01]
[3.5280249e-01 3.7495017e-01 3.9356515e-01 3.8802660e-01]
[3.6760172e-01 5.8778751e-01 4.2486581e-01 6.3663971e-01]
[3.6506388e-01 4.2663202e-01 3.9124954e-01 4.4209841e-01]
[4.1215658e-01 3.4202617e-01 8.8318264e-01 6.1285341e-01]
[3.4818730e-01 5.6928444e-01 3.6841545e-01 5.8634388e-01]
[3.5158402e-01 3.6752591e-01 3.9787045e-01 3.8494465e-01]
[3.6561641e-01 5.8981210e-01 3.8907713e-01 6.1613721e-01]
[3.4828454e-01 5.8140194e-01 3.6721477e-01 5.9566200e-01]
[3.4462604e-01 5.3514403e-01 3.9644474e-01 5.8370894e-01]
[3.6365288e-01 5.9567219e-01 3.9053750e-01 6.3109928e-01]
[3.5137120e-01 5.2337110e-01 3.7918985e-01 5.4226983e-01]
[4.9033237e-01 3.8423172e-01 6.1302155e-01 4.1986457e-01]
[4.1236705e-01 3.5832933e-01 4.8268488e-01 4.1150209e-01]
[4.1843241e-01 6.0260159e-01 4.9375936e-01 6.3197130e-01]
[3.4338030e-01 4.0502068e-01 3.6552405e-01 4.1651794e-01]
[4.2186460e-01 7.0060635e-01 8.9403832e-01 8.9042270e-01]
[3.4787107e-01 5.9927994e-01 3.6625677e-01 6.1748618e-01]
[7.1091282e-01 2.7190217e-01 8.2108068e-01 3.5610101e-01]
[3.3015379e-01 4.4123974e-01 3.4820625e-01 4.5039704e-01]
[3.2861453e-01 5.2156895e-01 3.4669027e-01 5.3986579e-01]
[4.9491951e-01 5.3556001e-01 7.1317983e-01 6.2238050e-01]
[3.1718564e-01 4.4188350e-01 3.3180881e-01 4.5035261e-01]
[3.7084836e-01 6.0645926e-01 4.4600427e-01 6.5543246e-01]
[3.8386708e-01 5.8912140e-01 4.2566502e-01 6.2365121e-01]
[3.5371971e-01 5.0196201e-01 3.8005614e-01 5.2139860e-01]
[5.9429705e-01 4.1528758e-01 6.5035301e-01 4.4080022e-01]
[3.6265263e-01 7.5203842e-01 4.6041501e-01 7.7500635e-01]
[3.4433401e-01 4.6549153e-01 3.6546969e-01 4.8306775e-01]
[3.8074300e-01 3.8487837e-01 4.5196366e-01 4.3506762e-01]
[2.5742817e-01 6.0831654e-01 2.8681454e-01 6.2162197e-01]
[7.1172494e-01 6.1941981e-01 8.8754946e-01 7.4794185e-01]
[5.6854314e-01 7.4925357e-01 7.2917712e-01 8.5470790e-01]
[3.6275440e-01 7.5717753e-01 4.6149722e-01 7.8934973e-01]
[5.5307239e-01 5.2523065e-01 6.6746283e-01 5.5445266e-01]
[3.1785089e-01 4.5512110e-01 3.3233309e-01 4.6439195e-01]
[4.1034609e-01 3.8362762e-01 4.7151357e-01 4.1939363e-01]
[3.3015379e-01 4.4123974e-01 3.4820625e-01 4.5039704e-01]
[5.6086457e-01 4.0125006e-01 6.4354509e-01 4.3456870e-01]
[7.1788090e-01 2.4195449e-01 8.1452870e-01 3.1889099e-01]
[3.5509771e-01 3.8778654e-01 3.9057735e-01 3.9996943e-01]]], shape=(1, 100, 4), dtype=float32)
# result내의 value들을 모두 numpy로 변환.
result = {key:value.numpy() for key,value in result.items()}
# 1부터 91까지의 COCO Class id 매핑.
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_detector(module_handle="https://tfhub.dev/tensorflow/efficientdet/d0/1"):
detector = hub.load(module_handle)
return detector
detector_model = get_detector()
WARNING:absl:Importing a function (__inference___call___32344) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_97451) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_77595) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_103456) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_93843) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_107064) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_75975) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
import cv2
img_array = cv2.cvtColor(cv2.imread('/content/data/beatles01.jpg'), cv2.COLOR_BGR2RGB)
# scaling된 이미지 기반으로 bounding box 위치가 예측 되므로 이를 다시 원복하기 위해 원본 이미지 shape정보 필요
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, ...]
# pretrained 모델을 다운로드 한 뒤 inference 수행.
result = detector_model(img_tensor)
# result 내부의 value를 numpy 로 변환.
result = {key:value.numpy() for key,value in result.items()}
SCORE_THRESHOLD = 0.3
OBJECT_DEFAULT_COUNT = 100
# detected 된 object들을 iteration 하면서 정보 추출. detect된 object의 갯수는 100개
for i in range(min(result['detection_scores'][0].shape[0], OBJECT_DEFAULT_COUNT)):
# detection score를 iteration시 마다 높은 순으로 추출하고 SCORE_THRESHOLD보다 낮으면 loop 중단.
score = result['detection_scores'][0, i]
if score < SCORE_THRESHOLD:
break
# detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
box = result['detection_boxes'][0, i]
''' **** 주의 ******
box는 ymin, xmin, ymax, xmax 순서로 되어 있음. '''
left = box[1] * width
top = box[0] * height
right = box[3] * width
bottom = box[2] * height
# class id 추출하고 class 명으로 매핑
class_id = result['detection_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)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.9484
person: 0.9401
person: 0.9359
person: 0.8954
car: 0.6267
car: 0.5109
truck: 0.3303
car: 0.3149
import time
def get_detected_img(model, img_array, score_threshold, object_show_count=100, is_print=True):
# scaling된 이미지 기반으로 bounding box 위치가 예측 되므로 이를 다시 원복하기 위해 원본 이미지 shape정보 필요
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, ...]
# efficientdet모델로 inference 수행.
start_time = time.time()
# inference 결과로 내부 원소가 Tensor이 Dict 반환
result = model(img_tensor)
# result 내부의 value를 numpy 로 변환.
result = {key:value.numpy() for key,value in result.items()}
# detected 된 object들을 iteration 하면서 정보 추출. detect된 object의 갯수는 100개
for i in range(min(result['detection_scores'][0].shape[0], object_show_count)):
# detection score를 iteration시 마다 높은 순으로 추출하고 SCORE_THRESHOLD보다 낮으면 loop 중단.
score = result['detection_scores'][0, i]
if score < score_threshold:
break
# detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
box = result['detection_boxes'][0, i]
''' **** 주의 ******
box는 ymin, xmin, ymax, xmax 순서로 되어 있음. '''
left = box[1] * width
top = box[0] * height
right = box[3] * width
bottom = box[2] * height
# class id 추출하고 class 명으로 매핑
class_id = result['detection_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
img_array = cv2.cvtColor(cv2.imread('/content/data/beatles01.jpg'), cv2.COLOR_BGR2RGB)
draw_img = get_detected_img(detector_model, img_array, score_threshold=0.4, object_show_count=100, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.9484
person: 0.9401
person: 0.9359
person: 0.8954
car: 0.6267
car: 0.5109
Detection 수행시간: 0.36 초
!wget -O ./data/baseball01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/baseball01.jpg
img_array = cv2.cvtColor(cv2.imread('/content/data/baseball01.jpg'), cv2.COLOR_BGR2RGB)
draw_img = get_detected_img(detector_model, img_array, score_threshold=0.4, object_show_count=100, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.9570
person: 0.9252
person: 0.9068
baseball glove: 0.6338
baseball bat: 0.5929
Detection 수행시간: 0.34 초
detector_model_d2 = get_detector('https://tfhub.dev/tensorflow/efficientdet/d2/1')
WARNING:absl:Importing a function (__inference_EfficientDet-D2_layer_call_and_return_conditional_losses_130857) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference___call___38449) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D2_layer_call_and_return_conditional_losses_145024) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_99017) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D2_layer_call_and_return_conditional_losses_139687) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_EfficientDet-D2_layer_call_and_return_conditional_losses_125520) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_101605) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
img_array = cv2.cvtColor(cv2.imread('/content/data/baseball01.jpg'), cv2.COLOR_BGR2RGB)
draw_img = get_detected_img(detector_model_d2, img_array, score_threshold=0.4, object_show_count=100, is_print=True)
plt.figure(figsize=(12, 12))
plt.imshow(draw_img)
person: 0.9488
person: 0.9321
person: 0.8895
baseball glove: 0.8303
baseball bat: 0.6166
sports ball: 0.5037
Detection 수행시간: 7.82 초
!wget -O ./data/Jonh_Wick_small.mp4 https://github.com/chulminkw/DLCV/blob/master/data/video/John_Wick_small.mp4?raw=true
def do_detected_video(model, input_path, output_path, score_threshold, is_print):
cap = cv2.VideoCapture(input_path)
codec = cv2.VideoWriter_fourcc(*'XVID')
vid_size = (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
vid_fps = cap.get(cv2.CAP_PROP_FPS)
vid_writer = cv2.VideoWriter(output_path, codec, vid_fps, vid_size)
frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print('총 Frame 갯수:', frame_cnt)
green_color=(0, 255, 0)
red_color=(0, 0, 255)
while True:
hasFrame, img_frame = cap.read()
if not hasFrame:
print('더 이상 처리할 frame이 없습니다.')
break
img_frame = get_detected_img(model, img_frame, score_threshold=score_threshold, object_show_count=100, is_print=is_print)
vid_writer.write(img_frame)
# end of while loop
vid_writer.release()
cap.release()
do_detected_video(detector_model, '/content/data/Jonh_Wick_small.mp4', './data/John_Wick_small_02.mp4', 0.5, True)
총 Frame 갯수: 58
car: 0.7054
car: 0.6741
car: 0.6634
car: 0.6490
Detection 수행시간: 0.3 초
car: 0.7121
car: 0.6782
car: 0.6640
car: 0.6493
Detection 수행시간: 0.31 초
car: 0.6018
motorcycle: 0.5719
car: 0.5339
person: 0.5157
car: 0.5006
person: 0.5002
Detection 수행시간: 0.33 초
motorcycle: 0.5697
car: 0.5522
car: 0.5182
person: 0.5053
Detection 수행시간: 0.3 초
car: 0.6813
motorcycle: 0.5480
car: 0.5432
person: 0.5387
Detection 수행시간: 0.3 초
car: 0.6372
motorcycle: 0.5610
car: 0.5422
Detection 수행시간: 0.29 초
car: 0.6398
motorcycle: 0.5655
car: 0.5501
car: 0.5255
Detection 수행시간: 0.28 초
car: 0.7404
car: 0.7017
car: 0.6398
motorcycle: 0.5679
person: 0.5268
Detection 수행시간: 0.29 초
car: 0.6955
car: 0.6933
car: 0.6458
motorcycle: 0.5257
Detection 수행시간: 0.29 초
car: 0.7060
car: 0.6844
car: 0.6742
motorcycle: 0.5419
Detection 수행시간: 0.29 초
car: 0.7386
car: 0.6630
car: 0.6616
motorcycle: 0.5867
Detection 수행시간: 0.29 초
car: 0.7788
car: 0.7119
car: 0.6659
motorcycle: 0.5716
Detection 수행시간: 0.29 초
car: 0.7903
person: 0.6799
car: 0.6535
car: 0.5919
motorcycle: 0.5642
person: 0.5333
Detection 수행시간: 0.29 초
car: 0.7585
car: 0.6350
person: 0.6308
person: 0.5748
car: 0.5594
Detection 수행시간: 0.29 초
car: 0.7656
person: 0.6383
car: 0.6349
car: 0.5677
person: 0.5621
motorcycle: 0.5030
Detection 수행시간: 0.28 초
car: 0.7444
car: 0.6351
person: 0.6318
car: 0.5491
motorcycle: 0.5110
Detection 수행시간: 0.3 초
car: 0.7239
car: 0.6659
person: 0.6601
motorcycle: 0.6324
person: 0.5235
Detection 수행시간: 0.29 초
car: 0.6936
person: 0.6540
car: 0.6427
motorcycle: 0.6184
Detection 수행시간: 0.29 초
person: 0.7235
car: 0.7211
car: 0.6419
motorcycle: 0.6291
car: 0.5875
car: 0.5150
Detection 수행시간: 0.29 초
car: 0.7176
person: 0.7174
car: 0.6390
motorcycle: 0.6266
car: 0.5862
car: 0.5188
Detection 수행시간: 0.3 초
car: 0.7741
person: 0.7211
motorcycle: 0.6405
car: 0.5664
car: 0.5461
person: 0.5386
Detection 수행시간: 0.28 초
car: 0.7878
person: 0.7171
car: 0.6266
motorcycle: 0.6054
car: 0.5526
Detection 수행시간: 0.28 초
car: 0.7586
car: 0.6461
car: 0.6202
person: 0.6173
person: 0.5188
car: 0.5160
Detection 수행시간: 0.31 초
person: 0.6640
person: 0.6427
car: 0.6098
horse: 0.6051
bicycle: 0.5354
Detection 수행시간: 0.27 초
person: 0.6494
person: 0.6488
horse: 0.6284
car: 0.5893
bicycle: 0.5526
Detection 수행시간: 0.28 초
person: 0.5867
person: 0.5624
bicycle: 0.5620
horse: 0.5345
Detection 수행시간: 0.29 초
person: 0.7165
car: 0.6705
horse: 0.5624
bicycle: 0.5571
person: 0.5267
Detection 수행시간: 0.3 초
person: 0.7392
horse: 0.6653
person: 0.6073
car: 0.5965
motorcycle: 0.5671
Detection 수행시간: 0.29 초
person: 0.8238
horse: 0.7039
car: 0.7006
motorcycle: 0.6065
person: 0.5841
Detection 수행시간: 0.28 초
person: 0.8135
horse: 0.7154
car: 0.7002
motorcycle: 0.6138
person: 0.5630
Detection 수행시간: 0.3 초
person: 0.8248
horse: 0.6896
person: 0.6198
car: 0.6137
motorcycle: 0.5657
Detection 수행시간: 0.29 초
person: 0.7325
horse: 0.6855
car: 0.6851
person: 0.6655
motorcycle: 0.6141
Detection 수행시간: 0.28 초
car: 0.7927
person: 0.7040
horse: 0.6783
person: 0.6495
motorcycle: 0.5853
Detection 수행시간: 0.27 초
car: 0.7433
person: 0.5885
person: 0.5693
bicycle: 0.5155
Detection 수행시간: 0.28 초
car: 0.7356
person: 0.5936
person: 0.5531
bicycle: 0.5072
Detection 수행시간: 0.28 초
car: 0.6917
person: 0.6702
person: 0.6286
motorcycle: 0.6036
Detection 수행시간: 0.29 초
car: 0.7995
person: 0.6749
motorcycle: 0.6159
person: 0.5632
horse: 0.5463
Detection 수행시간: 0.3 초
car: 0.7973
person: 0.6764
person: 0.6213
Detection 수행시간: 0.29 초
car: 0.8104
person: 0.6457
person: 0.6258
motorcycle: 0.5341
Detection 수행시간: 0.29 초
car: 0.8288
person: 0.6424
person: 0.6219
motorcycle: 0.5400
Detection 수행시간: 0.29 초
car: 0.8241
person: 0.6231
person: 0.5867
car: 0.5452
Detection 수행시간: 0.3 초
car: 0.8452
car: 0.7434
horse: 0.6338
person: 0.5680
person: 0.5553
Detection 수행시간: 0.29 초
car: 0.8632
car: 0.6939
horse: 0.6710
person: 0.5565
Detection 수행시간: 0.28 초
car: 0.8645
horse: 0.7133
car: 0.6535
person: 0.6077
Detection 수행시간: 0.29 초
car: 0.8637
horse: 0.7250
car: 0.6580
person: 0.6062
Detection 수행시간: 0.28 초
car: 0.8458
car: 0.6883
person: 0.6304
Detection 수행시간: 0.29 초
car: 0.8172
car: 0.5968
car: 0.5710
person: 0.5665
Detection 수행시간: 0.3 초
car: 0.7921
car: 0.6119
horse: 0.5238
Detection 수행시간: 0.28 초
car: 0.7709
car: 0.6165
person: 0.5716
car: 0.5662
horse: 0.5606
Detection 수행시간: 0.29 초
car: 0.7777
car: 0.6200
car: 0.5678
person: 0.5671
horse: 0.5564
Detection 수행시간: 0.29 초
car: 0.7557
horse: 0.5966
person: 0.5510
Detection 수행시간: 0.29 초
car: 0.7844
person: 0.5607
horse: 0.5251
Detection 수행시간: 0.3 초
car: 0.7524
person: 0.5303
Detection 수행시간: 0.3 초
car: 0.7289
car: 0.5995
Detection 수행시간: 0.3 초
car: 0.7325
car: 0.6154
Detection 수행시간: 0.3 초
car: 0.7161
car: 0.6772
car: 0.5412
horse: 0.5058
Detection 수행시간: 0.3 초
car: 0.7333
car: 0.7279
car: 0.6378
Detection 수행시간: 0.31 초
car: 0.8108
car: 0.7212
car: 0.7009
horse: 0.5520
Detection 수행시간: 0.29 초
더 이상 처리할 frame이 없습니다.
| 11-6~8. automl efficientDet (0) | 2021.11.30 |
|---|---|
| 11-4. tensorflow hub의 efficientDet lite 모델을 이용한 inference 실습 (0) | 2021.11.30 |
| 11-1. AutoML EfficientDet 패키지 소개 (0) | 2021.11.25 |
| 10-7. Compound Scaling (0) | 2021.10.29 |
| 10-6. EfficientNet (0) | 2021.10.29 |
이태원 두바이
난 중앙아시아 음식을 잘먹어서 양고기가 있었는데 누린내가 났지만 잘 먹었다
땅콩 수프가 있는데 한국인한테는 안맞는 것 같다
난 1500원이었는데 많이 주고, 맛있게 잘 먹었다
| 더현대 여의도 카멜커피 (0) | 2021.12.05 |
|---|---|
| 강남역 마마스 (0) | 2021.12.05 |
| 압구정동 테스트키친 (0) | 2021.11.30 |
| 더현대 디큐브 백미당 (2) | 2021.11.28 |
| 더현대 디큐브 라운드어바웃 (2) | 2021.11.28 |
압구정도 테스트키친
나 무화과 좋아하는데 매우 맛있었다 ㅎㅎ
| 강남역 마마스 (0) | 2021.12.05 |
|---|---|
| 이태원 두바이 (0) | 2021.11.30 |
| 더현대 디큐브 백미당 (2) | 2021.11.28 |
| 더현대 디큐브 라운드어바웃 (2) | 2021.11.28 |
| 베스킨라빈스 안국 (0) | 2021.11.26 |