-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathshirt_predict_pic.py
180 lines (147 loc) · 7.74 KB
/
shirt_predict_pic.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
# -*- coding:utf-8 -*-
__author__ = 'xuy'
import colorsys
import os
from timeit import default_timer as timer
import numpy as np
from PIL import Image, ImageFont, ImageDraw
from keras import backend as K
from keras.layers import Input
from yolo3.model import yolo_eval, yolo_body
from yolo3.utils import letterbox_image
class YOLO(object):
def __init__(self):
self.anchors_path = 'configs/yolo_anchors.txt' # Anchors
# self.model_path = 'model_data/yolo_weights.h5' # 模型文件
self.model_path = '/home/xuy/code/keras-yolo3-shirt_color/logs/trained_weights_final.h5' # 模型文件
# self.classes_path = 'configs/coco_classes.txt' # 类别文件
self.classes_path = 'configs/shirt.txt' # 类别文件
# self.model_path = 'model_data/ep074-loss26.535-val_loss27.370.h5' # 模型文件
# self.classes_path = 'configs/wider_classes.txt' # 类别文件
self.score = 0.15
# self.iou = 0.45
self.iou = 0.20
self.class_names = self._get_class() # 获取类别
self.anchors = self._get_anchors() # 获取anchor
self.sess = K.get_session()
self.model_image_size = (416, 416) # fixed size or (None, None), hw
self.boxes, self.scores, self.classes = self.generate()
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
def generate(self):
model_path = os.path.expanduser(self.model_path) # 转换~
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
num_anchors = len(self.anchors) # anchors的数量
num_classes = len(self.class_names) # 类别数
# 加载模型参数
self.yolo_model = yolo_body(Input(shape=(None, None, 3)), 3, num_classes)
self.yolo_model.load_weights(model_path)
print('{} model, {} anchors, and {} classes loaded.'.format(model_path, num_anchors, num_classes))
# 不同的框，不同的颜色
hsv_tuples = [(float(x) / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))] # 不同颜色
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) # RGB
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
# 根据检测参数，过滤框
self.input_image_shape = K.placeholder(shape=(2,))
boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors, len(self.class_names),
self.input_image_shape, score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
def detect_image(self, image):
start = timer() # 起始时间
if self.model_image_size != (None, None): # 416x416, 416=32*13，必须为32的倍数，最小尺度是除以32
assert self.model_image_size[0] % 32 == 0, 'Multiples of 32 required'
assert self.model_image_size[1] % 32 == 0, 'Multiples of 32 required'
boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size))) # 填充图像
else:
new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32))
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype='float32')
print('detector size {}'.format(image_data.shape))
image_data /= 255. # 转换0~1
image_data = np.expand_dims(image_data, 0) # 添加批次维度，将图片增加1维
# 参数盒子、得分、类别；输入图像0~1，4维；原始图像的尺寸
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for {}'.format(len(out_boxes), 'img')) # 检测出的框
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32')) # 字体
thickness = (image.size[0] + image.size[1]) // 128 # 厚度
predicted_class_list=[]
box_list=[]#用来存储坐标位置
score_list=[]#用来存储置信值
#由于有可能是预测多个类别，因此需要遍历模型，但是我所需要的是仅仅预测一个类别，因此先找出预测的置信值最大的，然后再在循环外面画框
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c] # 类别,str类型
box = out_boxes[i] # 框
score = out_scores[i] # 执行度
label = '{} {:.2f}'.format(predicted_class, score) # 标签
draw = ImageDraw.Draw(image) # 画图
label_size = draw.textsize(label, font) # 标签文字
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
print(label, (left, top), (right, bottom)) # 边框
predicted_class_list.append(predicted_class)
box_list.append([left,top,right,bottom])
score_list.append(score)
if top - label_size[1] >= 0: # 标签文字
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
if len(score_list)==0:
return None
#获取最大的置信值
max_index=score_list.index(max(score_list))
max_score=score_list[max_index]
max_predicted_class=predicted_class_list[max_index]
max_box=box_list[max_index]
max_left,max_top,max_right,max_bottom=max_box
print("更新之后的坐标标签是：",(max_left,max_top),(max_right,max_bottom))
label = '{} {:.2f}'.format(max_predicted_class, max_score) # 标签
#画图，需要拿到循坏外面，因为只有一个类别
# My kingdom for a good redistributable image drawing library.
for i in range(thickness): # 画框
draw.rectangle(
[max_left + i, max_top + i, max_right - i, max_bottom - i],
outline=self.colors[c])
draw.rectangle( # 文字背景
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font) # 文案
del draw
end = timer()
print(end - start) # 检测执行时间
return image
def close_session(self):
self.sess.close()
def detect_img_for_test(yolo):
img_path = '/home/xuy/code/keras-yolo3-shirt_color/people-det-base/JPEGImages/IMG_000067.jpg'
image = Image.open(img_path)
r_image = yolo.detect_image(image)
r_image.show()
r_image.save('IMG_000067.jpg')
yolo.close_session()
if __name__ == '__main__':
detect_img_for_test(YOLO())