30行代码让你玩转YOLO目标检测
30行代码让你玩转YOLO目标检测
深度学习与Python
发布于 2019-06-19 20:19:29
发布于 2019-06-19 20:19:29
YOLO是一种实时目标检测算法，与faster R-CNN不同的是，YOLO不是过多的注重物体定位的精度，而是着重于速度和物体种类的识别。在实际应用中，实时性是目标检测中非常重要的，例如对于一辆自动驾驶汽车来说，必须达到实时的检测红绿灯、行人和车辆等物体，这样才能安全的在街道上行驶。
YOLO算法在CNN的基础上定义网络结构，在使用时，我们可以直接加载其与训练的权重。其网络结构如下：
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64
2 conv 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32
3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
4 shortcut 1
5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128
6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
8 shortcut 5
9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
11 shortcut 8
12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256
13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
15 shortcut 12
16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
18 shortcut 15
19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
21 shortcut 18
22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
24 shortcut 21
25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
27 shortcut 24
28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
30 shortcut 27
31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
33 shortcut 30
34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
36 shortcut 33
37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512
38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
40 shortcut 37
41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
43 shortcut 40
44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
46 shortcut 43
47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
49 shortcut 46
50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
52 shortcut 49
53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
55 shortcut 52
56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
58 shortcut 55
59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
61 shortcut 58
62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024
63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
65 shortcut 62
66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
68 shortcut 65
69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
71 shortcut 68
72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
74 shortcut 71
75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
81 conv 255 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 255
82 detection
83 route 79
84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256
85 upsample * 2 13 x 13 x 256 -> 26 x 26 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256
88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
93 conv 255 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 255
94 detection
95 route 91
96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128
97 upsample * 2 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128
100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255
106 detection这里再介绍两个YOLO的重要的概念，一个是anchor boxes，另一个是NMS和IOU。
当在目标检测时，一个网格单元中可能会包含多个不同物体的中心点，即物体处于重叠状态，这是anchor boxes允许一个网格检测多个对象。效果如下图所示：
YOLO使用非最大抑制(NMS)来保留最佳边界框。NMS的第一步是删除检测概率小于给定NMS阈值的所有预测边界框。一般我们可以自己设置NMS值。在删除具有低检测概率的所有预测边界框之后，NMS中的第二步是选择具有最高检测概率的边界框，并消除其交叉联合（IOU）值高于给定值的所有边界框。
下面我们将通过一个小例子来实现YOLOv3算法，整个过程除去可视化代码外，整个识别部分代码不超过30行。具体步骤如下：
1、导入函数库，这里我们需要opencv读取图像、matplotlib绘图、utils以及darknet等。
2、读取YOLO网络权重，这样可以避免我们再次定义和训练模型。
3、读取图像并将待标记图像放缩到合适的尺寸
4、设置NMS值和IOU值
5、进行目标检测
结果如下：
完整代码：
Github地址：https://github.com/Garima13a/YOLO-Object-Detection/blob/master/YOLO.ipynb
import cv2
import matplotlib.pyplot as plt
from utils import *
from darknet import Darknet
# Set the location and name of the cfg file
cfg_file = './cfg/yolov3.cfg'
# Set the location and name of the pre-trained weights file
weight_file = './weights/yolov3.weights'
# Set the location and name of the COCO object classes file
namesfile = 'data/coco.names'
# Load the network architecture
m = Darknet(cfg_file)
# Load the pre-trained weights
m.load_weights(weight_file)
# Load the COCO object classes
class_names = load_class_names(namesfile)
# Print the neural network used in YOLOv3
m.print_network()
# Set the default figure size
plt.rcParams['figure.figsize'] = [24.0, 14.0]
# Load the image
img = cv2.imread('./images/dog.jpg')
# Convert the image to RGB
original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# We resize the image to the input width and height of the first layer of the network.
resized_image = cv2.resize(original_image, (m.width, m.height))
# Display the images
plt.subplot(121)
plt.title('Original Image')
plt.imshow(original_image)
plt.subplot(122)
plt.title('Resized Image')
plt.imshow(resized_image)
plt.show()
# Set the NMS threshold
nms_thresh = 0.6
# Set the IOU threshold
iou_thresh = 0.4
# Set the default figure size
plt.rcParams['figure.figsize'] = [24.0, 14.0]
# Load the image
img = cv2.imread('./images/dog.jpg')
# Convert the image to RGB
original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# We resize the image to the input width and height of the first layer of the network.
resized_image = cv2.resize(original_image, (m.width, m.height))
# Set the IOU threshold. Default value is 0.4
iou_thresh = 0.4
# Set the NMS threshold. Default value is 0.6
nms_thresh = 0.6
# Detect objects in the image
boxes = detect_objects(m, resized_image, iou_thresh, nms_thresh)
# Print the objects found and the confidence level
print_objects(boxes, class_names)
#Plot the image with bounding boxes and corresponding object class labelsplot_boxes(original_image, boxes, class_names, plot_labels = True)本文参与 腾讯云自媒体同步曝光计划，分享自微信公众号。
原始发表：2019-03-11，如有侵权请联系 cloudcommunity@tencent.com 删除
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