Object Detection using RetinaNet
RetinaNet是用來做目標檢測的，參考文獻 Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár.
後面將使用 Keras based implementation 訓練好的模型來進行測試，爲練集爲 COCO object detection dataset.
#正式版本與tensorflow2不兼容，安裝一個分支
%pip install -q git+https://github.com/fizyr/keras-retinanet
# git clone https://github.com/fizyr/keras-retinanet
#pip install .
加載預訓練好的模型
#所有預訓練好的模型可以從這裏下載：https://github.com/fizyr/keras-retinanet/releases
from urllib.request import urlretrieve
import os
PRETRAINED_MODEL_NAME = "resnet50_coco_best_v2.1.0.h5"
BACKBONE_NAME = 'resnet50'
PRETRAINED_BASE_URL = (
"https://github.com/fizyr/keras-retinanet/"
"releases/download/0.5.1/")
if not os.path.exists(PRETRAINED_MODEL_NAME):
model_url = PRETRAINED_BASE_URL + PRETRAINED_MODEL_NAME
print(f"Downloading {model_url}...")
urlretrieve(model_url, PRETRAINED_MODEL_NAME)
print("done.")
Downloading https://github.com/fizyr/keras-retinanet/releases/download/0.5.1/resnet50_coco_best_v2.1.0.h5...
done.
ls -lh *.h5
-rw-rw-r-- 1 tianliang tianliang 146M Feb 5 23:38 resnet50_coco_best_v2.1.0.h5
from keras_retinanet import models
# load retinanet model
model = models.load_model(PRETRAINED_MODEL_NAME, backbone_name=BACKBONE_NAME)
Using TensorFlow backend.
tracking <tf.Variable 'Variable:0' shape=(9, 4) dtype=float32, numpy=
array([[-22.627417, -11.313708, 22.627417, 11.313708],
[-28.50876 , -14.25438 , 28.50876 , 14.25438 ],
[-35.918785, -17.959393, 35.918785, 17.959393],
[-16. , -16. , 16. , 16. ],
[-20.158737, -20.158737, 20.158737, 20.158737],
[-25.398417, -25.398417, 25.398417, 25.398417],
[-11.313708, -22.627417, 11.313708, 22.627417],
[-14.25438 , -28.50876 , 14.25438 , 28.50876 ],
[-17.959393, -35.918785, 17.959393, 35.918785]], dtype=float32)> anchors
tracking <tf.Variable 'Variable:0' shape=(9, 4) dtype=float32, numpy=
array([[-45.254833, -22.627417, 45.254833, 22.627417],
[-57.01752 , -28.50876 , 57.01752 , 28.50876 ],
[-71.83757 , -35.918785, 71.83757 , 35.918785],
[-32. , -32. , 32. , 32. ],
[-40.317474, -40.317474, 40.317474, 40.317474],
[-50.796833, -50.796833, 50.796833, 50.796833],
[-22.627417, -45.254833, 22.627417, 45.254833],
[-28.50876 , -57.01752 , 28.50876 , 57.01752 ],
[-35.918785, -71.83757 , 35.918785, 71.83757 ]], dtype=float32)> anchors
tracking <tf.Variable 'Variable:0' shape=(9, 4) dtype=float32, numpy=
array([[ -90.50967 , -45.254833, 90.50967 , 45.254833],
[-114.03504 , -57.01752 , 114.03504 , 57.01752 ],
[-143.67514 , -71.83757 , 143.67514 , 71.83757 ],
[ -64. , -64. , 64. , 64. ],
[ -80.63495 , -80.63495 , 80.63495 , 80.63495 ],
[-101.593666, -101.593666, 101.593666, 101.593666],
[ -45.254833, -90.50967 , 45.254833, 90.50967 ],
[ -57.01752 , -114.03504 , 57.01752 , 114.03504 ],
[ -71.83757 , -143.67514 , 71.83757 , 143.67514 ]],
dtype=float32)> anchors
tracking <tf.Variable 'Variable:0' shape=(9, 4) dtype=float32, numpy=
array([[-181.01933, -90.50967, 181.01933, 90.50967],
[-228.07008, -114.03504, 228.07008, 114.03504],
[-287.35028, -143.67514, 287.35028, 143.67514],
[-128. , -128. , 128. , 128. ],
[-161.2699 , -161.2699 , 161.2699 , 161.2699 ],
[-203.18733, -203.18733, 203.18733, 203.18733],
[ -90.50967, -181.01933, 90.50967, 181.01933],
[-114.03504, -228.07008, 114.03504, 228.07008],
[-143.67514, -287.35028, 143.67514, 287.35028]], dtype=float32)> anchors
tracking <tf.Variable 'Variable:0' shape=(9, 4) dtype=float32, numpy=
array([[-362.03867, -181.01933, 362.03867, 181.01933],
[-456.14017, -228.07008, 456.14017, 228.07008],
[-574.70056, -287.35028, 574.70056, 287.35028],
[-256. , -256. , 256. , 256. ],
[-322.5398 , -322.5398 , 322.5398 , 322.5398 ],
[-406.37466, -406.37466, 406.37466, 406.37466],
[-181.01933, -362.03867, 181.01933, 362.03867],
[-228.07008, -456.14017, 228.07008, 456.14017],
[-287.35028, -574.70056, 287.35028, 574.70056]], dtype=float32)> anchors
/home/tianliang/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/saving.py:341: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.
warnings.warn('No training configuration found in save file: '
#print(model.summary())
對測試圖片進行檢測（分類和位置）
#labels to names of coco dataset
labels_to_names = {
0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane',
5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light',
10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench',
14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow',
20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack',
25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee',
30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite',
34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard',
37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass',
41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed',
60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse',
65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave',
69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book',
74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear',
78: 'hair drier', 79: 'toothbrush'}
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption
from keras_retinanet.utils.colors import label_color
import matplotlib.pyplot as plt
import time
import cv2
import numpy as np
def detect_and_visualize(image_bgr):
# copy to draw on
draw = image_bgr.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# preprocess image for network
image_input = image_bgr.copy()
image_input = preprocess_image(image_input)
image_input, scale = resize_image(image_input)
print(f"shape: {image_input.shape}, dtype: {image_input.dtype}, "
f"range: {(image_input.min(), image.max())}")
# process image
start = time.time()
boxes, scores, labels = model.predict_on_batch(
np.expand_dims(image_input, axis=0))
print(f"processing time: {time.time() - start:.1f}s")
# correct for image scale
boxes /= scale
# visualize detections
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < 0.4:
break
color = label_color(label)
b = box.astype(int)
draw_box(draw, b, color=color)
caption = "{} {:.3f}".format(labels_to_names[label], score)
draw_caption(draw, b, caption)
plt.figure(figsize=(8, 8))
plt.axis('off')
plt.imshow(draw)
# load image
image = read_image_bgr('test.png')
detect_and_visualize(image)
shape: (749, 1333, 3), dtype: float32, range: (-123.67999, 255)
processing time: 0.1s
使用筆記本電腦的攝像頭
由於使用的是服務器，沒有攝像頭，這裏只放代碼
import cv2
from keras_retinanet.utils.image import read_image_bgr
import matplotlib.pyplot as plt
def camera_grab(camera_id=0, fallback_filename='test.png'):
camera = cv2.VideoCapture(camera_id)
try:
# 連排10張，便攝像頭自動調整，期望最後一張有好的效果
for i in range(10):
snapshot_ok, image = camera.read()
if not snapshot_ok:
print("WARNING: could not access camera")
if fallback_filename:
image = read_image_bgr(fallback_filename)
finally:
camera.release()
return image
image = camera_grab(camera_id=0)
plt.figure(figsize=(8, 8))
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis('off');
WARNING: could not access camera
detect_and_visualize(image)
shape: (749, 1333, 3), dtype: float32, range: (-123.67999, 255)
processing time: 0.1s
[外鏈圖片轉存中…(img-MEYuVFst-1581136017420)]
detect_and_visualize(image)
shape: (749, 1333, 3), dtype: float32, range: (-123.67999, 255)
processing time: 0.1s
[外鏈圖片轉存中…(img-bHfIUEnT-1581136017423)]
