def detect_classify_modified(self):
"""
detect and classify
"""
# print(self.imgs_path)
for tracklet in self.imgs_path:
tracklet_camera_path = [
os.path.join(tracklet, x) for x in os.listdir(tracklet)
]
for tracklet_camera in tracklet_camera_path:
the_imgs_path = [
os.path.join(tracklet_camera, x)
for x in os.listdir(tracklet_camera) if x.endswith('.jpg')
]
# print(the_imgs_path)
for the_img in the_imgs_path:
# print(the_img)
# read image data
img = cv2.imread(the_img)
img = cv2.copyMakeBorder(img,
BORDER,
BORDER,
BORDER,
BORDER,
cv2.BORDER_CONSTANT,
value=(100, 100, 100))
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction, self.prob_th,
self.num_classes,
self.nms_th, self.inp_dim,
orig_img_size)
orig_img = cv2.cvtColor(np.asarray(img),
cv2.COLOR_RGB2BGR) # RGB => BGR
print(the_img)
try:
if type(output) != int:
self.cls_draw_bbox(output, orig_img)
print('\n', os.path.split(the_img)[0])
dst_path = self.dst_dir + '/' + os.path.split(
the_img)[0] + '/' + os.path.split(the_img)[1]
print(dst_path)
if not os.path.exists(dst_path):
cv2.imwrite(dst_path, orig_img)
except Exception as inst:
img.show()
print(inst)
exit(2)
def detect_classify(self):
"""
detect and classify
"""
for x in self.imgs_path:
# read image data
img = Image.open(x)
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction,
self.prob_th,
self.num_classes,
self.nms_th,
self.inp_dim,
orig_img_size)
orig_img = cv2.cvtColor(np.asarray(
img), cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
self.cls_draw_bbox(output, orig_img)
dst_path = self.dst_dir + '/' + os.path.split(x)[1]
if not os.path.exists(dst_path):
cv2.imwrite(dst_path, orig_img)
def predict(self):
"""
批量检测和识别, 将检测, 识别结果输出到dst_dir
"""
for x in self.imgs_path:
# 读取图像数据
img = Image.open(x)
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # 图像数据放到device
# 车辆检测
prediction = self.Net.forward(img2det, CUDA=True)
# 计算scaling factor
orig_img_size = list(img.size)
output = process_predict(prediction, self.prob_th,
self.num_classes, self.nms_th,
self.inp_dim, orig_img_size)
orig_img = cv2.cvtColor(np.asarray(img),
cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
# 将检测框bbox绘制到原图上
# draw_car_bbox(output, orig_img)
self.cls_draw_bbox(output, orig_img)
# self.cls_and_draw(output, img)
dst_path = self.dst_dir + '/' + os.path.split(x)[1]
if not os.path.exists(dst_path):
cv2.imwrite(dst_path, orig_img)
def detect_classify(self):
"""
detect and classify
"""
for x in self.imgs_path:
# read image data
img = Image.open(x)
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction,
self.prob_th,
self.num_classes,
self.nms_th,
self.inp_dim,
orig_img_size)
#orig_img = cv2.cvtColor(np.asarray(
#img), cv2.COLOR_RGB2BGR) # RGB => BGR
#if type(output) != int:
#self.cls_draw_bbox(output, orig_img)
#dst_path = self.dst_dir + '/' + os.path.split(x)[1]
#if not os.path.exists(dst_path):
#cv2.imwrite(dst_path, orig_img)
# [left-up(x),left-up(y),right-down(x),right-down(y)] --当检测到的目标大于一个
x_left = output[:, 1].item()
y_left = output[:, 2].item()
x_right = output[:, 3].item()
y_right = output[:, 4].item()
# centriod[x,y]
x_centriod = (x_left + x_right) / 2
y_centriod = (y_left + y_right) / 2
w_rect = x_right - x_left
h_rect = y_right - y_left
# 4 corners point
x_leftup = x_centriod - w_rect/2
y_leftup = y_centriod - h_rect/2
x_leftdown = x_centriod - w_rect / 2
y_leftdown = y_centriod + h_rect / 2
x_rightup = x_centriod + w_rect / 2
y_rightup = y_centriod - h_rect / 2
x_rightdown = x_centriod + w_rect / 2
y_rightdown = y_centriod + h_rect / 2
# new lists to deposit the 4 corners point
leftup = [int(x_leftup), int(y_leftup)]
leftdown = [int(x_leftdown), int(y_leftdown)]
rightup = [int(x_rightup), int(y_rightup)]
rightdown = [int(x_rightdown), int(y_rightdown)]
return leftup, leftdown, rightup, rightdown
def detect_classify_video(self, video_path, res_path):
"""
detect in video frames
"""
# myvideo = cv2.VideoCapture(video_path)
# retval = cv2.VideoCapture.grab()
# 获得视频的格式
videoCapture = cv2.VideoCapture(video_path)
# 获得码率及尺寸
fps = videoCapture.get(cv2.CAP_PROP_FPS)
size = (int(videoCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(videoCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
# 指定写视频的格式, I420-avi, MJPG-mp4
videoWriter = cv2.VideoWriter(
res_path, cv2.VideoWriter_fourcc('X', 'V', 'I', 'D'), fps, size)
# 读帧
success, frame = videoCapture.read()
while success:
# cv2.imshow("Oto Video", frame) #显示
cv2.waitKey(int(1000 / int(fps))) # 延迟
# 检测图片
img = cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR)
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=use_cuda)
# calculating scaling factor
orig_img_size = list(size)
output = self.process_predict(prediction, self.prob_th,
self.num_classes, self.nms_th,
self.inp_dim, orig_img_size)
orig_img = cv2.cvtColor(np.asarray(img),
cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
self.cls_draw_bbox(output, orig_img)
videoWriter.write(orig_img) # 写视频帧
success, frame = videoCapture.read() # 获取下一帧
def test_car_detect(car_cfg_path='./car.cfg',
car_det_weights_path='g:/Car_DR/car_360000.weights'):
"""
imgs_path: 图像数据路径
"""
inp_dim = 768
prob_th = 0.2 # 车辆检测概率阈值
nms_th = 0.4 # NMS阈值
num_cls = 1 # 只检测车辆1类
# 初始化车辆检测模型及参数
Net = Darknet(car_cfg_path)
Net.load_weights(car_det_weights_path)
Net.net_info['height'] = inp_dim # 车辆检测输入分辨率
Net.to(device)
Net.eval() # 测试模式
print('=> car detection model initiated.')
# 读取图像数据
img = Image.open(
'f:/FaceRecognition_torch_0_4/imgs_21/det_2018_08_21_63_1.jpg')
img2det = process_img(img, inp_dim)
img2det = img2det.to(device) # 图像数据放到device
# 测试车辆检测
prediction = Net.forward(img2det, CUDA=True)
# 计算scaling factor
orig_img_size = list(img.size)
output = process_predict(prediction, prob_th, num_cls, nms_th, inp_dim,
orig_img_size)
orig_img = np.asarray(img)
if type(output) != int:
# 将检测框bbox绘制到原图上
draw_car_bbox(output, orig_img)
cv2.imshow('test', orig_img)
cv2.waitKey()
def detect_classify(self):
"""
detect and classify
"""
#read and save video
cap = cv2.VideoCapture(self.video_path)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(self.dst_path, fourcc, 25.0, (960, 540))
while(cap.isOpened()):
# read image data
ret, x = cap.read()
re_img = cv2.resize(x, (960, 540), cv2.INTER_LINEAR)
img = Image.fromarray(cv2.cvtColor(re_img, cv2.COLOR_BGR2RGB))
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction,
self.prob_th,
self.num_classes,
self.nms_th,
self.inp_dim,
orig_img_size)
orig_img = cv2.cvtColor(np.asarray(
img), cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
orig_img = self.cls_draw_bbox(output, orig_img)
orig_img = self.car_recognition(output, orig_img)
out.write(orig_img)
cap.release()
out.release()
def detect_classify(self, query_pair):
pre_path = ''
color_dict = {}
type_dict = {}
# cars = []
# all_cars_per_camera = {}
index_list_all = []
index_list_per_camera = []
pre_camera_id = self.imgs_path[0].split('/')[3]
stream_i = 0
print("\n\nProcessing stream %d...\n" % stream_i)
tracklet_i = 0
"""
detect and classify
"""
for x in self.imgs_path:
curr_path = os.path.split(x)[0]
# read image data
img = cv2.imread(x)
img = cv2.copyMakeBorder(img,
BORDER,
BORDER,
BORDER,
BORDER,
cv2.BORDER_CONSTANT,
value=(100, 100, 100))
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction, self.prob_th,
self.num_classes, self.nms_th,
self.inp_dim, orig_img_size)
orig_img = cv2.cvtColor(np.asarray(img),
cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
# print('\n', x)
car_color, car_direction, car_type = self.cls_draw_bbox(
output, orig_img)
dst_path = self.dst_dir + '/' + os.path.split(x)[1]
# if not os.path.exists(dst_path):
# cv2.imwrite(dst_path, orig_img)
if curr_path != pre_path and pre_path != '':
start_length = os.path.split(os.path.split(pre_path)[0])[1]
detect_color = max(color_dict, key=color_dict.get)
detect_type = max(type_dict, key=type_dict.get)
print("Tracklet %d detects " % tracklet_i, detect_color,
detect_type)
# add_to_all(all_cars_per_camera, detect_color, detect_type)
compare_query_append(query_pair, detect_color, detect_type,
index_list_per_camera, tracklet_i,
start_length)
tracklet_i += 1
color_dict.clear()
type_dict.clear()
curr_camera_id = x.split('/')[3]
if curr_camera_id != pre_camera_id:
print("The query result on stream %d:" % stream_i,
index_list_per_camera)
index_list_all.append(deepcopy(index_list_per_camera))
index_list_per_camera.clear()
pre_camera_id = curr_camera_id
stream_i += 1
tracklet_i = 0
print("\n\nProcessing stream %d...\n" % stream_i)
if car_color != None:
if car_color not in color_dict:
color_dict[car_color] = 0
color_dict[car_color] += 1
if car_type != None:
if car_type not in type_dict:
type_dict[car_type] = 0
type_dict[car_type] += 1
pre_path = curr_path
# add the last one
if pre_path != '':
start_length = os.path.split(os.path.split(pre_path)[0])[1]
detect_color = max(color_dict, key=color_dict.get)
detect_type = max(type_dict, key=type_dict.get)
print("Tracklet %d detects " % tracklet_i, detect_color,
detect_type)
compare_query_append(query_pair, detect_color, detect_type,
index_list_per_camera, tracklet_i,
start_length)
# print(all_cars_per_camera)
color_dict.clear()
type_dict.clear()
print("The query result on stream %d:" % stream_i,
index_list_per_camera)
index_list_all.append(deepcopy(index_list_per_camera))
return index_list_all
