def detect_image(self, image):
if self.is_fixed_size:
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
return_boxs = []
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
if predicted_class != 'person' :
continue
box = out_boxes[i]
# score = out_scores[i]
x = int(box[1])
y = int(box[0])
w = int(box[3]-box[1])
h = int(box[2]-box[0])
if x < 0 :
w = w + x
x = 0
if y < 0 :
h = h + y
y = 0
return_boxs.append([x,y,w,h])
return return_boxs
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
return image
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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(" image_data.shape:",image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
center_y = (top + bottom) / 2
center_x = (left + right) / 2
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.point((center_x, center_y), fill=(255, 0, 0))
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
# print("时间：",end - start)
return image
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None): #设置检测图片的大小，即输入神经网络的图片大小
assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required' #可以和实际大小不一致，但必须是32的倍数
assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size))) #letterbox_image返回根据model_image_size缩放大小的图片
else: #如果不设置model_image_size，则去掉32的余数之后，按照实际大小输入神经网络
new_image_size = (image.width - (image.width % 32),
image.height - (image.height % 32))
boxed_image = letterbox_image(image, new_image_size) #返回按照实际尺寸去掉32余数之后的图片大小
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
print('sess run before',self.input_image_shape)
print(image_data.shape, image.size)
out_boxes, out_scores, out_classes = self.sess.run( #out_boxes是所有检测框的坐标，out_scores是该检测框的得分，out_classes是检测到的类别
[self.boxes, self.scores, self.classes],
feed_dict={ #feed_dict表示临时生效
self.yolo_model.input: image_data, #image_data是经过参数model_image_size缩放了的图片数据
self.input_image_shape: [image.size[1], image.size[0]], #image.size是原始图片大小数据
K.learning_phase(): 0 #0表示训练模式，1表示测试模式
})
print('sess run after',self.input_image_shape)
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
led_result = open('.\\logs\\led_result.txt', 'w')
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c] + str(i+1)
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
led_result.write(str(label) + '/' + str(left) + ',' + str(top) + ',' + str(right) + ',' + str(bottom) + '\n')
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print('The time is:',end - start)
led_result.close()
return image
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
file_name = '_results.csv'
csvFile = open(file_name, 'w',newline = '')
writer = csv.writer(csvFile)
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor((3e-2 * image.size[1] + 0.5)*0.8).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
infor = [predicted_class, score, left, top, right, bottom]
writer.writerow(infor)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
#draw.rectangle(
# [tuple(text_origin), tuple(text_origin + label_size)],
# fill=self.colors[c])
#draw.text(text_origin, label, fill=(0, 0, 0), font=font)
#del draw
# Do not show the labels!
end = timer()
print(end - start)
return image
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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))) #letterbox()标准化尺寸？？
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
global num_cone
num_cone=len(out_boxes)
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
global Class
Class=c
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
#calculate distance to the cones
global locX,locY
locX=round((left+right)/2)
locY=round(top+(bottom-top)*0.8)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.ellipse(((left+right)/2,(top+(bottom-top)*0.8) , 5, 5), fill=self.colors[c])
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
#import cv2
#cv2.imshow("detected",image)
return image
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
#将右下坐标转换成w h
# out_boxes[0][2] = out_boxes[0][2] - out_boxes[0][0]
# out_boxes[0][3]=out_boxes[0][3]-out_boxes[0][1]
# print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
#
# font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
# size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
# thickness = (image.size[0] + image.size[1]) // 300
#
# for i, c in reversed(list(enumerate(out_classes))):
# predicted_class = self.class_names[c]
# box = out_boxes[i]
# score = out_scores[i]
#
# label = '{} {:.2f}'.format(predicted_class, score)
# draw = ImageDraw.Draw(image)
# label_size = draw.textsize(label, font)
#
# 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))
#
# if top - label_size[1] >= 0:
# text_origin = np.array([left, top - label_size[1]])
# else:
# text_origin = np.array([left, top + 1])
#
# # My kingdom for a good redistributable image drawing library.
# for i in range(thickness):
# draw.rectangle(
# [left + i, top + i, right - i, bottom - i],
# outline=self.colors[c])
# draw.rectangle(
# [tuple(text_origin), tuple(text_origin + label_size)],
# fill=self.colors[c])
# draw.text(text_origin, label, fill=(0, 0, 0), font=font)
# del draw
# end = timer()
# print(end - start)
# return image
return out_boxes
def detect_image(self, image):
start = timer() # 开始计时
if self.model_image_size != (None, None):
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)))
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(image_data.shape) # 打印图片的尺寸
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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')) # 提示用于找到几个bbox
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(2e-2 * image.size[1] +
0.2).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 500
# 保存框检测出的框的个数
# file.write('find ' + str(len(out_boxes)) + ' target(s) \n')
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
font = ImageFont.truetype("simsun.ttc", 20, encoding='unic')
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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'))
# 写入检测位置
# file.write(
# predicted_class + ' score: ' + str(score) + ' \nlocation: top: ' + str(top) + '、 bottom: ' + str(
# bottom) + '、 left: ' + str(left) + '、 right: ' + str(right) + '\n')
file.write(' ' + str(self.class_names.index(predicted_class) + 1) +
' ' + str(score) + ' ' + str(left) + ' ' + str(top) +
' ' + str(right) + ' ' + str(bottom) + ';')
print(label, (left, top), (right, bottom))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
# font = ImageFont.truetype("simsun.ttc", 20, encoding='unic')
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print('time consume:%.3f s ' % (end - start))
return image
def detect_image(self, image, csv_list):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
if len(out_scores) == 0:
return None, None
i = 0
c = out_classes[0]
print(out_scores)
# print(i)
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
# add csv data written by nakatani
csv_list.append(label.split()[1])
# socket block written by nakatani
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
# サーバを指定
s.connect(('127.0.0.1', 90))
# サーバにメッセージを送る
# s.sendall(b'hello')
send_message = label + ":"
s.sendall(send_message.encode("UTF-8"))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
image = image.crop((left, top, right, bottom))
# show bounding box 171 - 201 written by nakatani
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start, "seconds takes")
return image, csv_list
def detect_image_pic(self, image): #检测每一张图片的人脸位置
start = timer() # 起始时间
pic_filename = os.path.basename(img_path)
# txt_filename=pic_filename.replace("jpg","txt")
portion = os.path.splitext(pic_filename)
if portion[1] == '.jpg':
txt_result = predict_result + portion[0] + '.txt'
print('txt_result的路径是：' + txt_result)
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
with open(txt_result, 'a') as new_f:
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c] # 类别
box = out_boxes[i] # 框
score = out_scores[i] # 执行度
label = '{} {:.2f}'.format(predicted_class, score) # 标签,是预测概率值
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,这个就是【置信值，xmin,ymin,xmax,ymax】,可以做一下mAP值的分析了
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
# 这里需要改
new_f.write(
str(label) + " " + str(max_left) + " " + str(max_top) + " " +
str(max_right) + " " + str(max_bottom) + '\n')
# 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( # 文字背景是红色
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0),
font=font) # 文字内容，face+是人脸的概率值
del draw
end = timer()
print(end - start) # 检测执行时间
return image
def eval(self,
step,
eval_images_path,
ground_truth_path,
tag='image',
is_save_images=True):
# Add the class predict temp dict
class_pred_tmp = {}
for class_name in self.class_names:
class_pred_tmp[class_name] = []
# Predict!!!
for start in range(0, len(eval_images_path), self.step2_batch_size):
end = start + self.step2_batch_size
images_path = eval_images_path[start:end]
images = []
images_org = []
images_shape = []
files_id = []
for image_path in images_path:
image = Image.open(image_path)
file_id = os.path.split(image_path)[-1].split('.')[0]
boxed_image = letterbox_image(
image, tuple(reversed(self.input_shape)))
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
images_shape.append([image.size[1], image.size[0]])
images.append(image_data)
images_org.append(image)
files_id.append(file_id)
images = np.array(images)
out_bboxes_1, out_bboxes_2, out_bboxes_3 = self.yolo_body.predict_on_batch(
images)
for i, out in enumerate(
zip(out_bboxes_1, out_bboxes_2, out_bboxes_3)):
# Predict
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
# self.eval_inputs: out
self.eval_inputs[0]:
np.expand_dims(out[0], 0),
self.eval_inputs[1]:
np.expand_dims(out[1], 0),
self.eval_inputs[2]:
np.expand_dims(out[2], 0),
self.input_image_shape:
images_shape[i]
})
image = np.array(images_org[i])
ord_h = image.shape[0]
ord_w = image.shape[1]
new_h = int(image.shape[0] * 3 / 4)
new_w = int(image.shape[1] * 3 / 4)
image = cv2.resize(image, (new_w, new_h))
for o, c in enumerate(out_classes):
predicted_class = self.class_names[c]
box = out_boxes[o]
score = out_scores[o]
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(ord_h, np.floor(bottom + 0.5).astype('int32'))
right = min(ord_w, np.floor(right + 0.5).astype('int32'))
bbox = "{} {} {} {}".format(left, top, right, bottom)
class_pred_tmp[predicted_class].append({
"confidence":
str(score),
"file_id":
files_id[i],
"bbox":
bbox
})
# Draw image
new_top, new_left, new_bottom, new_right = (
box * 3 / 4).astype('int32')
if files_id[i] in self.eval_save_images_id:
label = '{} {:.2f}'.format(predicted_class, score)
# print(label, (left, top), (right, bottom))
color = self.colors[c]
cv2.rectangle(image, (new_left, new_top),
(new_right, new_bottom), color, 2)
font_size = math.sqrt((new_right - new_left) *
(new_bottom - new_top)) / 50
if font_size > 0.8:
font_size = 0.8
elif font_size < 0.3:
font_size = 0.3
cv2.putText(image, label, (new_left, new_top - 3),
cv2.FONT_HERSHEY_SIMPLEX, font_size, color,
1)
if is_save_images:
if files_id[i] in self.eval_save_images_id:
log_images(self.callback, tag + '/' + files_id[i],
[image], step)
# Create predict temp
for class_name in self.class_names:
with open(
self.tmp_pred_files_path + "/" + class_name +
"_predictions.json", 'w') as outfile:
json.dump(class_pred_tmp[class_name], outfile)
# calculate the AP for each class
sum_AP = 0.0
count_true_positives = {}
for class_index, class_name in enumerate(
sorted(self.gt_counter_per_class.keys())):
count_true_positives[class_name] = 0
# load predictions of that class
predictions_file = self.tmp_pred_files_path + "/" + class_name + "_predictions.json"
predictions_data = json.load(open(predictions_file))
# Assign predictions to ground truth objects
nd = len(predictions_data) # number of predict data
tp = [0] * nd # true positive
fp = [0] * nd # false positive
for idx, prediction in enumerate(predictions_data):
file_id = prediction["file_id"]
gt_file = ground_truth_path + "/" + file_id + "_ground_truth.json"
ground_truth_data = json.load(open(gt_file))
ovmax = -1
gt_match = -1
# load prediction bounding-box
bb = [float(x) for x in prediction["bbox"].split()]
for obj in ground_truth_data:
# look for a class_name match
if obj["class_name"] == class_name:
bbgt = [float(x) for x in obj["bbox"].split()]
# Area of Overlap
bi = [
max(bb[0], bbgt[0]),
max(bb[1], bbgt[1]),
min(bb[2], bbgt[2]),
min(bb[3], bbgt[3])
]
iw = bi[2] - bi[0] + 1
ih = bi[3] - bi[1] + 1
# compute overlap (IoU) = area of intersection / area of union
if iw > 0 and ih > 0:
# Area of Union
ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + \
(bbgt[2] - bbgt[0] + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
ov = iw * ih / ua
if ov > ovmax:
ovmax = ov
gt_match = obj
if ovmax >= self.min_overlap:
if not gt_match['used']:
tp[idx] = 1
gt_match["used"] = True
# count_true_positives[predicted_class] += 1
with open(gt_file, 'w') as f:
f.write(json.dumps(ground_truth_data))
else:
fp[idx] = 1
else:
fp[idx] = 1
# compute precision/recall
cumsum = 0
for idx, val in enumerate(fp):
fp[idx] += cumsum
cumsum += val
cumsum = 0
for idx, val in enumerate(tp):
tp[idx] += cumsum
cumsum += val
rec = tp[:]
for idx, val in enumerate(tp):
rec[idx] = float(
tp[idx]) / self.gt_counter_per_class[class_name]
prec = tp[:]
for idx, val in enumerate(tp):
prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])
ap, mrec, mprec = self.voc_ap(rec, prec)
sum_AP += ap
log_scalar(self.callback, tag + '_mAP/' + class_name, ap * 100,
step)
mAP = sum_AP / len(self.gt_counter_per_class)
log_scalar(self.callback, 'Total_mAP/' + tag, mAP * 100, step)
# remove the tmp_files directory
shutil.rmtree(self.tmp_pred_files_path)
shutil.rmtree(ground_truth_path)
os.mkdir(self.tmp_pred_files_path)
shutil.copytree(ground_truth_path + '_org', ground_truth_path)
return mAP * 100
def detect_image(self,
image,
single_image=True,
output=list(),
bbox_set=list(),
label_set=list(),
score_set=list(),
speed=list()):
if self.model_image_size != (None, None):
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)))
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')
if single_image: print(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
start = timer()
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
})
end = timer()
if single_image:
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
# Label & Score font Size.
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(6e-2 * image.size[1] +
0.5).astype('int32'))
# Bounding Box thickness.
thickness = max(1, (image.size[0] + image.size[1]) // 300)
for i, c in (list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
old_top, old_left, old_bottom, old_right = box
top = max(0, np.floor(old_top + 0.5).astype('int32'))
left = max(0, np.floor(old_left + 0.5).astype('int32'))
bottom = min(image.size[1],
np.floor(old_bottom + 0.5).astype('int32'))
right = min(image.size[0],
np.floor(old_right + 0.5).astype('int32'))
if single_image:
print(label, (old_left, old_top), (old_right, old_bottom))
else:
y1 = int(round(old_top))
x1 = int(round(old_left))
y2 = int(round(old_bottom))
x2 = int(round(old_right))
output_str = '{} {} {} {} {}'.format(label, y1, x1, y2, x2)
output.append(output_str)
bbox_set.append(([y1, x1, y2, x2]))
label_set.append(int(predicted_class))
score_set.append(score)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
#end = timer()
print(end - start)
speed.append(float(end - start))
if single_image: print(end - start)
return image
def generate(self, FLAGS):
model_path = os.path.expanduser(FLAGS['model'])
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
try:
model = tf.keras.models.load_model(model_path, compile=False)
except:
if self.backbone == BACKBONE.MOBILENETV2:
model_body = partial(mobilenetv2_yolo_body,
alpha=FLAGS['alpha'])
elif self.backbone == BACKBONE.DARKNET53:
model_body = darknet_yolo_body
elif self.backbone == BACKBONE.EFFICIENTNET:
model_body = partial(efficientnet_yolo_body,
model_name='efficientnet-b4')
if tf.executing_eagerly():
model = model_body(tf.keras.layers.Input(
shape=(*self.input_shape, 3), name='predict_image'),
num_anchors=num_anchors // 3,
num_classes=num_classes)
else:
self.input = tf.keras.layers.Input(shape=(None, None, 3),
name='predict_image',
dtype=tf.uint8)
input = tf.map_fn(
lambda image: tf.image.convert_image_dtype(
image, tf.float32), self.input, tf.float32)
image, shape = letterbox_image(input, self.input_shape)
self.input_image_shape = tf.shape(input)[1:3]
image = tf.reshape(image, [-1, *self.input_shape, 3])
model = model_body(image,
num_anchors=num_anchors // 3,
num_classes=num_classes)
model.load_weights(
model_path) # make sure model, anchors and classes match
else:
assert model.layers[-1].output_shape[-1] == \
num_anchors / len(model.output) * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
print('{} model, anchors, and classes loaded.'.format(model_path))
# Generate colors for drawing bounding boxes.
if tf.executing_eagerly():
self.yolo_model = model
else:
output = YoloEval(self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.nms,
name='yolo')(model.output)
# output = tf.keras.layers.Lambda(lambda input: yolo_eval(
# input,
# self.anchors,
# len(self.class_names),
# self.input_image_shape,
# score_threshold=self.score,
# iou_threshold=self.nms),name='yolo')(model.output)
self.yolo_model = tf.keras.Model(model.input, output)
# Generate output tensor targets for filtered bounding boxes.
hsv_tuples: List[Tuple[float, float, float]] = [
(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))
]
self.colors: List[Tuple[float, float, float]] = list(
map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors: List[Tuple[int, int, int]] = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
np.random.seed(10101) # Fixed seed for consistent colors across runs.
np.random.shuffle(
self.colors) # Shuffle colors to decorrelate adjacent classes.
np.random.seed(None) # Reset seed to default.
def detect_image(self, image, draw=True) -> Image:
if tf.executing_eagerly():
image_data = tf.expand_dims(image, 0)
if self.input_shape != (None, None):
boxed_image, image_shape = letterbox_image(
image_data, tuple(reversed(self.input_shape)))
else:
height, width, _ = image_data.shape
new_image_size = (width - (width % 32), height - (height % 32))
boxed_image, image_shape = letterbox_image(
image_data, new_image_size)
image_data = np.array(boxed_image)
start = timer()
output = self.yolo_model.predict(image_data)
out_boxes, out_scores, out_classes = yolo_eval(
output,
self.anchors,
len(self.class_names),
image.shape[0:2],
score_threshold=self.score,
iou_threshold=self.nms)
end = timer()
image = Image.fromarray((np.array(image) * 255).astype('uint8'),
'RGB')
else:
image_data = np.expand_dims(image, 0)
start = timer()
out_boxes, out_scores, out_classes = self.sess.run(
self.yolo_model.output, feed_dict={self.input: image_data})
end = timer()
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
if draw:
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[1] + image.size[0]) // 300
draw = ImageDraw.Draw(image)
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
label_size = draw.textsize(label, font)
top, left, bottom, right = box
print(label, (left, top), (right, bottom))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
print(end - start)
return image
else:
return out_boxes, out_scores, out_classes
def evaluate(self, annotation_path, iou_threshold=0.5, save_img=False):
'''Evaluate a given dataset using a given model.
# Arguments
model : The model to evaluate.
annotation_path : each row in imgset_lable file like this:
imgpath x_min1,y_min1,x_max1,y_max1,0 x_min2,y_min2,x_max2,y_max2,2......
iou_threshold : The threshold used to consider when a detection is positive or negative,
using for mAP, not the same as iou of NMS for detections.
# Returns
A dict mapping class names to mAP scores.
'''
with open(annotation_path) as f:
annotation_lines = f.readlines()
all_detections = [[None for i in range(len(self.class_names))]
for j in range(len(annotation_lines))]
all_annotations = [[None for i in range(len(self.class_names))]
for j in range(len(annotation_lines))]
start = timer()
for i in range(len(annotation_lines)):
line = annotation_lines[i].split()
image = Image.open(line[0])
boxes = np.array([
np.array(list(map(int, boxes.split(','))))
for boxes in line[1:]
])
for label in range(len(self.class_names)):
all_annotations[i][label] = boxes[boxes[:, -1] == label, :-1]
if self.model_image_size != (None, None):
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)))
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')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
out_boxes2 = out_boxes.copy()
for j in range(len(out_boxes2)):
box = out_boxes2[j]
top, left, bottom, right = box
out_boxes2[j][1] = max(0, np.floor(top + 0.5).astype('int32'))
out_boxes2[j][0] = max(0, np.floor(left + 0.5).astype('int32'))
out_boxes2[j][3] = min(image.size[1],
np.floor(bottom + 0.5).astype('int32'))
out_boxes2[j][2] = min(image.size[0],
np.floor(right + 0.5).astype('int32'))
image_detections = np.concatenate([
out_boxes2,
np.expand_dims(out_scores, axis=1),
np.expand_dims(out_classes, axis=1)
],
axis=1)
for label in range(len(self.class_names)):
all_detections[i][label] = image_detections[
image_detections[:, -1] == label, :-1]
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
if save_img:
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for m, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[m]
score = out_scores[m]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for n in range(thickness):
draw.rectangle(
[left + n, top + n, right - n, bottom - n],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
image.save('results/val/val_%d.jpg' % (i))
end = timer()
print(end - start)
return self._get_map(all_detections,
all_annotations,
iou_threshold=iou_threshold)
def detect_image(self, image, num):
start = timer()
humanplace = []
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
tempframe = str(num) + '\n'
hunamnum = 0
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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'))
dian_y = int((top + bottom) / 2)
dian_x = int((left + right) / 2)
temppoint = [dian_x, dian_y]
#print(label)
if 'person' in label:
temp = label + ' (' + str(left) + ',' + str(
top) + ') (' + str(right) + ',' + str(bottom) + ')' + '\n'
humanplace.append([left, top, right, bottom])
hunamnum = hunamnum + 1
tracknum.append(temppoint)
#print(label, (left, top), (right, bottom))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
if 'person' in label:
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
pointdraw = ImageDraw.Draw(image)
for i in tracknum:
print(len(tracknum))
pointdraw.ellipse((i[0] - 5, i[1] - 5, i[0] + 5, i[1] + 5),
(255, 0, 0))
del pointdraw
end = timer()
#print(end - start)
return image, humanplace
def detect_image(self, image):
"""
利用yolov3网络对图片进行识别
注意 对于cv2来说，h,w,channel = shape[0],shape[1],shape[2]
args:
image: ndarray
returns:
image: ndarray
out_boxes:
out_scores:
out_classes:
"""
start = timer()
if self.model_image_size != (None, None):
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)))
else:
# 这里也要改过来
# new_image_size = (image.width - (image.width % 32),
# image.height - (image.height % 32))
new_image_size = (image.height - (image.height % 32),
image.width - (image.width % 32))
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
# return out_boxes, out_scores, out_classes
font = ImageFont.truetype(
font=os.path.join(gParam.Font_Path, 'ARLRDBD.TTF'),
size=np.floor(2e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 600
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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'))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
# print('Time:', end-start)
return image, out_boxes, out_scores, out_classes
def detect_image(self, image):
"""if self.model_image_size != (None, None):
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)))"""
if self.model_image_size != (None, None):
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)))
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')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(
font='/usr/share/fonts/truetype/freefont/FreeMono.ttf',
size=np.floor(8e-2 * image.size[1] - 20).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
left, top, right, bottom, c = -1, -1, -1, -1, -1
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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), " c: ", c, " i: ", i)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
return c, left, top, right, bottom, image
def detect_image(self, image):
start = timer()
start1 = timer()
if self._model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
print("preprocess: %fs" % (timer() - start1))
start1 = timer()
yolo_outputs = self._sess.run(self._output_tensor_list,
feed_dict={
self._input_tensor: image_data
})
print("model inference: %fs" % (timer() - start1))
start1 = timer()
out_boxes, out_scores, out_classes = yolo_eval(yolo_outputs, self._anchors,
len(self._class_names), [image.size[1], image.size[0]],
score_threshold=self._score_thresh,
iou_threshold=self._nms_iou_thresh)
print("cpu post process: %fs" % (timer() - start1))
print("total time: %fs" % (timer() - start))
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 400
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self._class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for j in range(thickness):
draw.rectangle(
[left + j, top + j, right - j, bottom - j],
outline=self._colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self._colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
print("")
return image
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
history = open('history_object_detection.txt', 'a')
db = 'test.db'
outputBuffer = BytesIO()
image.save(outputBuffer, format='JPEG')
imageBase64Data = outputBuffer.getvalue()
data = base64.b64encode(imageBase64Data)
outputBuffer.close
sql.add_record(db, data)
#sql.extr_record(db, './output', 1)
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# Закоментировать что бы убрать отрисовку прямоугольников
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
class_object = label.split(' ')[0]
def save_object():
area = (left, top, right, bottom)
cropped_image = image.crop(area)
cropped_image.save(self.object_path + class_object +
'/frame_' + str(self.counter_image) + '_' +
label + '.jpeg')
outputBuffer = BytesIO()
cropped_image.save(outputBuffer, format='JPEG')
imageBase64Data = outputBuffer.getvalue()
data = base64.b64encode(imageBase64Data)
outputBuffer.close
edit_score = '{:.2f}'.format(score)
face_id = ''
face_accuracy = 0
# Face ID request
if label.count('person'):
url = 'https://api.evision.tech/predict/2.0' # Set destination URL here
post_fields = {
"key": "",
"minAccuracy": 30,
"type": "check",
"headers": {
'Content-Type': "application/json"
},
"image": "data:image/jpeg;base64," + str(data)[2:]
}
request = Request(url, urlencode(post_fields).encode())
resultRequest = json.loads(
urlopen(request).read().decode())
print(resultRequest)
sql.add_record_class(db, predicted_class)
if (resultRequest and resultRequest['success']
and resultRequest['data']
and len(resultRequest['data'])
and resultRequest['data'][0]
and resultRequest['data'][0]['id']
and resultRequest['data'][0]['accuracy']):
face_id = resultRequest['data'][0]['id']
face_accuracy = resultRequest['data'][0]['accuracy']
# print('face_accuracy = ', face_accuracy)
sql.add_record_child(db, predicted_class, edit_score,
data, face_id, face_accuracy)
#sql.child_extr_record(db, './output', 1)
history.write('Class: ' + class_object + ' Frame: ' +
str(self.counter_image) + ' Probability: ' +
label + '\r\n')
if not os.path.exists(self.object_path + class_object):
try:
os.makedirs(self.object_path + class_object)
save_object()
except OSError as e:
if e.errno != errno.EEXIST:
raise
else:
save_object()
history.write('\r\n')
history.close()
end = timer()
print(end - start)
image.save(self.frames_path + str(self.counter_image) + '.jpeg')
self.counter_image += 1
return image
def detect_image(
self, image
): #uncomment lines 110,126,127 if helmet detection not needed
start = timer()
#'''
img_arr = np.array(image)
img_arr1 = np.array(image)
frame_count = 0
inpWidth = 416 #Width of network's input image
inpHeight = 416 #Height of network's input image
# Create a 4D blob from a frame.
blob = cv2.dnn.blobFromImage(img_arr,
1 / 255, (inpWidth, inpHeight), [0, 0, 0],
1,
crop=False)
# Sets the input to the network
hdy.net.setInput(blob)
# Runs the forward pass to get output of the output layers
outs = hdy.net.forward(hdy.getOutputsNames(hdy.net))
# Remove the bounding boxes with low confidence
helmet_boxes, score_boxes = hdy.postprocess('', img_arr, outs, '',
True)
#'''
#helmet_boxes = []
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
first = 0
for ind, hbx in enumerate(helmet_boxes):
if first == 0:
#print(helmet_boxes)
first = 1
hbx = [hbx[1], hbx[0], hbx[1] + hbx[3], hbx[0] + hbx[2]]
a = np.array(hbx)
a = np.reshape(a, (1, a.size))
out_boxes = np.concatenate((out_boxes, a))
out_classes = np.concatenate((out_classes, np.array([80])))
out_scores = np.concatenate(
(out_scores, np.array([score_boxes[ind]])))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
all_boxes = []
for i, c in reversed(list(enumerate(out_classes))):
if c != 80:
predicted_class = self.class_names[c]
else:
predicted_class = 'Helmet'
c = 35 #for colour purpose
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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'))
all_boxes.append([label, left, top, right, bottom])
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
#print(end - start)
bike_boxes = []
rider_boxes = []
helmet_boxes = []
#print(all_boxes) #label,left,top,right,bottom
for box in all_boxes:
label, left, top, right, bottom = box
if 'motorbike' in label.lower():
bike_boxes.append([left, top, right, bottom])
elif 'person' in label.lower():
rider_boxes.append([left, top, right, bottom])
elif 'helmet' in label.lower():
helmet_boxes.append([left, top, right, bottom])
for bb in bike_boxes:
flag = 0
for rb in rider_boxes:
l1 = Point(bb[0], bb[3]) #590,157
r1 = Point(bb[2], bb[1]) #668, 270
l2 = Point(rb[0], rb[3]) #631, 42
r2 = Point(rb[2], rb[1]) #704, 236
if (doOverlap(l1, r1, l2, r2)):
for hb in helmet_boxes:
l1 = Point(hb[0], hb[3]) #590,157
r1 = Point(hb[2], hb[1]) #668, 270
l2 = Point(rb[0], rb[3]) #631, 42
r2 = Point(rb[2], rb[1]) #704, 236
if (doOverlap(l1, r1, l2, r2)):
flag = 1
break
if not flag:
for rb in rider_boxes:
l1 = Point(bb[0], bb[3]) #590,157
r1 = Point(bb[2], bb[1]) #668, 270
l2 = Point(rb[0], rb[3]) #631, 42
r2 = Point(rb[2], rb[1]) #704, 236
if (doOverlap(l1, r1, l2, r2)):
#crop
global ctr1
ctr1 += 1
pair = [rb, bb]
I = cv2.cvtColor(img_arr1, cv2.COLOR_BGR2RGB)
image2 = Image.fromarray(I, mode='RGB')
im_crop = image2.crop(
(max(pair[1][0] - 30,
0), max(pair[0][1] - 30,
0), pair[1][2] + 30, pair[1][3] + 30))
# Comment out below line to disable challan images popping up during execution
# todo control this using argparse
im_crop.show()
#parts = img.rsplit('.',1)
#im_crop.save(os.path.join('cropped_images',parts[0]+str(ctr)+'.'+parts[1]))
im_crop.save(
os.path.join('cropped_images',
str(ctr1) + '.jpg'))
sleep(1)
#im_crop.close()
for proc in psutil.process_iter():
if proc.name() == "display":
proc.kill()
break
return image, all_boxes
def detect_image_single(self, file, isDrawBox):
try:
image = Image.open(file)
width, height = image.size
except Exception as e:
print("Error: ", repr(e))
return None
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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('type of out_boxes: ', type(out_boxes))
# print('type of out_scores: ', type(out_scores))
# print('type of out_classes: ', type(out_classes))
#
# print(zip(out_boxes, out_scores, out_classes))
# for i, (a, b, c) in enumerate(zip(out_boxes, out_scores, out_classes)):
# print(i, a, b, self.class_names[c])
#series = pd.Series(out_boxes[:, 0])
df = pd.DataFrame(out_boxes)
#df = df.rename(columns={'0':'top', '1':'left', '2':'bottom','3':'right'})
df.columns = ['top', 'left', 'bottom', 'right']
#print(np.floor(df['top'] + 0.5))
df['top'] = np.maximum(0, np.floor(df['top'] + 0.5)).astype('int32')
df['left'] = np.maximum(0, np.floor(df['left'] + 0.5)).astype('int32')
df['bottom'] = np.minimum(height, np.floor(df['bottom'] + 0.5)).astype('int32')
df['right'] = np.minimum(width, np.floor(df['right'] + 0.5)).astype('int32')
#print(np.minimum(0, np.floor(df['top'] + 0.5)))
class_names = []
for i in out_classes:
class_names.append(self.class_names[i])
df['class_name'] = class_names
df['score'] = out_scores
df['out_classes'] = out_classes
df['image'] = os.path.basename(file)
df['area_pct'] = (df['bottom'] - df['top']) * (df['right'] - df['left']) / (width * height)
df['area_pct'] = df['area_pct'].abs()
df['x_min'] = df['left']
df['y_min'] = df['top']
df['width'] = df['right'] - df['left']
df['height'] = df['bottom'] - df['top']
# print(df)
# print(df['class_name'])
if isDrawBox:
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
#
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'))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
# if df.ix[i, 'top'] - label_size[1] >= 0:
# text_origin = np.array([df.ix[i, 'left'], df.ix[i, 'top'] - label_size[1]])
# else:
# text_origin = np.array([df.ix[i, 'left'], df.ix[i, 'top'] + 1])
#
# # My kingdom for a good redistributable image drawing library.
# #print(label, (left, top), (right, bottom))
# for i in range(thickness):
# draw.rectangle(
# [df.ix[i, 'left'] + i, df.ix[i, 'top'] + i, df.ix[i, 'right'] - i, df.ix[i, 'bottom'] - i],
# outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
return df, image
def detect_image(self, image):
"""
A function which returns the predictions of a trained yolo model together with
all the information that is necessary for computing the mAP scores wrt the ground truth files.
We also return the original image with a bounding-box drawn on it if it exists.
:param image: an image coming from the testing-set
:return: the predicted_class, x_min, y_min, x_max, y_max and an image with the drawn bounding-boxes
"""
detections = []
min_coordinates = []
max_coordinates = []
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='../font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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'))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
detections.append(label)
min_coordinates.append((left, top))
max_coordinates.append((right, bottom))
del draw
return detections, min_coordinates, max_coordinates, image
def detecting_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
prediction_path = self.prediction_path
if os.path.exists(prediction_path) == False:
os.mkdir(prediction_path)
print('make folder: ', prediction_path)
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'))
for i, c in reversed(list(enumerate(out_classes))):
top, left, bottom, right = out_boxes[i]
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'))
# draw.rectangle([left, top, right, bottom], outline=self.colors[1])
# del draw
cropbox = (left, top, right, bottom)
cropped = image.crop(cropbox)
predicted_class = self.class_names[c]
imageName = '{}_{}_{}_{}_{}.jpg'.format(predicted_class, left, top,
right, bottom)
prediction_image = os.path.join(prediction_path, imageName)
print('Saving: ', prediction_image)
cropped.save(prediction_image)
print(predicted_class)
print(out_scores[i])
end = timer()
print('time used: %f' % (end - start))
return
def detect_image(self, image, dir):
start = timer()
name = dir.split('\\')
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
image_crop = image
count = 0 # add
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
image_crop = image.crop((left, top, right, bottom))
print(left, top, right, bottom)
#draw = ImageDraw.Draw(image_crop)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
image_crop.save('./' + str(count) + "_" + "(" + str(left) + "," +
str(top) + ")" + "(" + str(right) + "," +
str(bottom) + ")" + "_" + name[-1])
count = count + 1
# My kingdom for a good redistributable image drawing library.
""" (draw detect box )20190306
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
"""
end = timer()
print(end - start)
return image_crop
def detect_image(self, image, query_input, frame_id):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
# Initialize a counter to count car per frame
count = 0
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# check if predicted object is a car
if(predicted_class == "car"):
# get the region of interest indices
boundry_box = (left, top, right, bottom)
# Crop image to region of interest
cropped_image = self.crop_image(image,boundry_box)
# Query 1
if(query_input == '1'):
# Start timer
prev_time = timer()
# Increment count
count += 1
# Add Count to the label
label = '{}, count = {}'.format(predicted_class, count)
label_size = draw.textsize(label, font)
# Draw bounding box
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
# Stop timer
curr_time = timer()
# Record execution time
time = curr_time - prev_time
# Add time and count details of the frame to the dictionary
self.result_dictionary[frame_id] = {'count':count, 'time': time}
elif(query_input == '2'):
# Start timer
prev_time = timer()
# Increment count
count += 1
# get car type
carType = self.carTypeObj.predict_image(cropped_image)
# Add car type and count to the label
label = '{}, count = {}, {}'.format(predicted_class, count, carType)
label_size = draw.textsize(label, font)
# Draw bounding box
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
# Stop Timer
curr_time = timer()
# Record execution time
time = curr_time - prev_time
# Add car type, count and time details of the frame to the dictionary
self.result_dictionary[frame_id] = {'count': count, carType: 1, 'time': time}
elif(query_input == '3'):
# Start timer
prev_time = timer()
# Increment count
count += 1
# get car type
carType = self.carTypeObj.predict_image(cropped_image)
# get car color
carColor = self.colorObj.detect_color(cropped_image)
# Add color, type and count of the car to the label
label = '{}, count = {}, {}, {}'.format(predicted_class, count, carType, carColor)
label_size = draw.textsize(label, font)
# Draw bounding box
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
# Stop Timer
curr_time = timer()
# Record execution time
time = curr_time - prev_time
# Add color, type, count and time details of the frame to the dictionary
self.result_dictionary[frame_id] = {'count': count, carType: 1, carColor: 1, 'time': time}
else:
print("Query Input Received : ", query_input)
count = 0
end = timer()
print(end - start)
return image
def detect_with_onnx(self, image):
if self.model_image_size != (None, None):
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)))
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')
image_data /= 255.
image_data = np.transpose(image_data, [2, 0, 1])
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
feed_f = dict(
zip(['input_1', 'image_shape'],
(image_data,
np.array([image.size[1], image.size[0]],
dtype='float32').reshape(1, 2))))
all_boxes, all_scores, indices = self.session.run(None,
input_feed=feed_f)
out_boxes, out_scores, out_classes = [], [], []
for idx_ in indices:
out_classes.append(idx_[1])
out_scores.append(all_scores[tuple(idx_)])
idx_1 = (idx_[0], idx_[2])
out_boxes.append(all_boxes[idx_1])
font = ImageFont.truetype(
font=self._get_data_path('font/FiraMono-Medium.otf'),
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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'))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
return image
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), 'image')) # 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]) // 512 # 厚度
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c] # 类别
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
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])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness): # 画框
draw.rectangle([left + i, top + i, right - i, 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 button_open_image_click(self):
imgName, imgType = QFileDialog.getOpenFileName(
self, "打开图片", "", "*.jpg;;*.png;;All Files(*)")
image = Image.open(imgName)
print("单幅图像", image.mode)
# r_image = detect_image(image)
# r_image.show()
if self.model_image_size != (None, None):
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)))
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(" image_data.shape:",image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
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))
center_y = (top + bottom) / 2
center_x = (left + right) / 2
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.point((center_x, center_y), fill=(255, 0, 0))
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
self.result = np.asarray(image)
self.result = cv2.cvtColor(self.result, cv2.COLOR_RGB2BGR)
# cv2.imwrite('1.jpg',self.result)
self.result = cv2.cvtColor(self.result, cv2.COLOR_BGR2BGRA)
self.result = cv2.resize(self.result, (640, 480),
interpolation=cv2.INTER_AREA)
# self.result = cv2.cvtColor(self.result, cv2.COLOR_BGRA2RGB)
# result = frame
self.QtImg = QtGui.QImage(self.result.data, self.result.shape[1],
self.result.shape[0],
QtGui.QImage.Format_RGB32)
# 显示图片到label中;
self.showimage.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))
def detect_image(self, image):
if self.is_fixed_size:
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
return_boxs = []
return_class_name = []
person_counter = 0
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
#print(self.class_names[c])
'''
if predicted_class != 'person' and predicted_class != 'car':
print(predicted_class)
continue
'''
if predicted_class != args["class"]:
#print(predicted_class)
continue
person_counter += 1
#if predicted_class != 'car':
#continue
#label = predicted_class
box = out_boxes[i]
#score = out_scores[i]
x = int(box[1])
y = int(box[0])
w = int(box[3] - box[1])
h = int(box[2] - box[0])
if x < 0:
w = w + x
x = 0
if y < 0:
h = h + y
y = 0
return_boxs.append([x, y, w, h])
#print(return_boxs)
return_class_name.append([predicted_class])
#cv2.putText(image, str(self.class_names[c]),(int(box[0]), int(box[1] -50)),0, 5e-3 * 150, (0,255,0),2)
#print("Found person: ",person_counter)
return return_boxs, return_class_name
def detect_from_img(self, image, frame):
start = timer()
#origin_img = image
#origin_img = cv2.cvtColor(np.asarray(origin_img), cv2.COLOR_RGB2BGR)
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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
})
#out_boxes = [i for i in out_boxes if i in ['bus','car','person']]
#out_scores = [i for i in out_scores if i in ['bus','car','person']]
out_classes = [i for i in out_classes if self.class_names[i] in ['bus','car','person']]
result_info = 'Found {} boxes:\n'.format(len(out_boxes))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 400
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
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'))
if predicted_class == 'car' or predicted_class == 'bus':
box = [top, bottom, left, right]
print(type(frame))
carnumber = self.detect_carnumber(frame, box, [20,20])
if carnumber is None:
carnumber = 'no'
else:
carnumber = ''.join(carnumber)
print('carnumber',carnumber)
label = '{} {}'.format(predicted_class, carnumber) #label = '{} {:.2f}'
else:
label = '{} {:.1f}'.format(predicted_class, score) #label = '{} {:.2f}'
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
result_info += label
result_info += '\n'
print(label)#(left, top), (right, bottom)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
return image, result_info
def detect_image(self, image):
start = timer()
if self.model_image_size != (None, None):
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)))
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(image_data.shape)
image_data /= 255.
#print('m1')
image_data = np.expand_dims(image_data, 0)
#print('m2')
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('m3');
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
top, left, bottom, right = box
top = max(
0,
np.floor(top + 0.5).astype('int32') -
0.07 * np.floor(top + 0.5).astype('int32'))
left = max(
0,
np.floor(left + 0.5).astype('int32') -
0.07 * np.floor(left + 0.5).astype('int32'))
bottom = min(
image.size[1],
np.floor(bottom + 0.5).astype('int32') +
0.07 * np.floor(bottom + 0.5).astype('int32'))
right = min(
image.size[0],
np.floor(right + 0.5).astype('int32') +
0.07 * np.floor(right + 0.5).astype('int32'))
print(label, (left, top), (right, bottom))
#if (right-left / bottom-top > 2):
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
return image