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_boxes = []
return_scores = []
return_class_names = []
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
# if ((predicted_class != 'car') or (predicted_class != 'person') or (predicted_class != 'traffic light'))==True: # Modify to detect other classes.
# continue
box = out_boxes[i]
score = out_scores[i]
# print(score)
if score < 0.3: # Modify to detect other classes.
continue
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_boxes.append([x, y, w, h])
return_scores.append(score)
return_class_names.append(predicted_class)
return return_boxes, return_scores, return_class_names
def detect_image(self, image, model_image_size=(608, 608)):
start = timer()
boxed_image = letterbox_image(image, tuple(reversed(model_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.yolo4_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,cv2_img, model_image_size=(608, 608)):
start = timer()
boxed_image = letterbox_image(image, tuple(reversed(model_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.yolo4_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
# COLORS = np.random.uniform(0, 255, size=(len(self.class_names), 3)).astype('int32')
inf = timer()
print('INference Time:-',inf-start)
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)
label_size = cv2.getTextSize(label,cv2.FONT_HERSHEY_COMPLEX,0.5,2)
print(label_size)
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):
color = self.colors[c]
print(label, (left, top), (right, bottom),image.size[0],image.size[1])
cv2.rectangle(cv2_img, (left,top), (right,bottom),color, 2)
cv2.putText(cv2_img, label, tuple(text_origin), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2)
# 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)
# cv2.imshow('Detections',draw)
# del draw
end = timer()
print(end - start)
return cv2_img
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
})
#print(reversed(list(enumerate(out_classes))))
return_boxes = []
return_scores = []
return_class_names = []
return_plates = []
return_cuts = []
car_num = 0
person_num = 0
plate = ''
cut_name = ''
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
if predicted_class not in ('person', 'car', 'motorbike', 'bicycle',
'bus', 'traffic light'):
continue
elif predicted_class in ('car', 'bus'):
car_num += 1
#检测车牌区域
if (out_scores[i] > 0.7):
cut = image.crop(
(int(out_boxes[i][1]), int(out_boxes[i][0]),
int(out_boxes[i][3]), int(out_boxes[i][2])))
cut.save('cut' + str(i) + '.jpg')
cut_name = 'cut' + str(i) + '.jpg'
#appcode_demo.demo('cut'+str(i)+'.jpg')
cut = numpy.array(cut)
l = HyperLPR_plate_recognition(cut)
#l = pp.SimpleRecognizePlate(cut)
if l.__len__() != 0:
print('l', l)
#if (l[0][1] > 0.85):
plate = [l[0][0], l[0][1]]
# print(plate,l[0][1])
elif predicted_class == 'person':
person_num += 1
elif predicted_class == 'traffic light':
#检测红绿灯颜色
cut = image.crop((int(out_boxes[i][1]), int(out_boxes[i][0]),
int(out_boxes[i][3]), int(out_boxes[i][2])))
cut = numpy.array(cut)
color = self.get_color(cut)
predicted_class = str(color)
print(color)
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_boxes.append([x, y, w, h])
return_scores.append(score)
return_class_names.append(predicted_class)
return_plates.append(plate)
return_cuts.append(cut_name)
plate = ''
cut_name = ''
return return_boxes, return_scores, return_class_names, return_plates, car_num, person_num, return_cuts
def detect_image(self, image, model_image_size=(608, 608)):
start = timer()
boxed_image = letterbox_image(image, tuple(reversed(model_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.yolo4_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
listPositions = {}
listLaybel = {}
p = {}
ll = []
la = []
z = 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 * 100)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
sc = '{:.2f}%'.format(score * 100)
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))
name = label
rs = (predicted_class, left, top, right, bottom)
p = {
"left": int(left),
"top": int(top),
"right": int(right),
"bottom": int(bottom)
}
# listPositions['item'+str(z)] = {
# 'name': name
# }
z += 1
listLaybel['item' + str(z)] = name
ll.append({'name': predicted_class, 'score': sc, 'position': p})
# cv2.rectangle(image, (int(left), int(top)),
# (int(right), int(bottom)), (0, 255, 0), 5)
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
image.save('img/abc.jpg')
print(Path().absolute())
end = timer()
return ll
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')
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]
if predicted_class != 'person' \
and predicted_class != 'bicycle' \
and predicted_class != 'car' \
and predicted_class != 'motorbike'\
and predicted_class != 'bus'\
and predicted_class != 'truck'\
and predicted_class != 'stop sign'\
and predicted_class != 'traffic light':
# and predicted_class != ''\
# and predicted_class != '':
print(predicted_class)
continue
# if predicted_class != args["class"]:#and predicted_class != 'car':
# #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
