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detect_videofile.py
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detect_videofile.py
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import argparse
import torch.backends.cudnn as cudnn
from models.experimental import *
from utils.datasets import *
# from utils.utils import *
from utils.general import (
check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, plot_one_box, strip_optimizer)
from utils.torch_utils import select_device, load_classifier, time_synchronized,initialize_weights
# from modelsori import *
def point_in_box(points,polygon):
inside=False
x,y=points
xmin,ymin,xmax,ymax=polygon
if x >xmin and x< xmax and y>ymin and y< ymax:
inside=True
return inside
def detect(save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
half=False
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
# model = Darknet('cfg/prune_0.8_yolov3-spp.cfg', (opt.img_size, opt.img_size)).to(device)
# initialize_weights(model)
# model.load_state_dict(torch.load('weights/prune_0.8_yolov3-spp-ultralytics.pt')['model'])
# model.eval()
# stride = [8, 16, 32]
# imgsz = check_img_size(imgsz, s=max(stride)) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
# if webcam:
# view_img = True
# cudnn.benchmark = True # set True to speed up constant image size inference
# dataset = LoadStreams(source, img_size=imgsz)
# else:
# save_img = True
# dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
# names = ['1', '2']
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
videopath_list = (
# '2020double_company',
# '2020double_company_1',
# 'child_79_company',
# 'child_137_huaxia',
# 'child_137_huaxia_1',
# 'double_54_zhuhai',
# 'double_54_zhuhai_1',
# 'double_59_huaxiaxueyuan',
# 'double_59_huaxiaxueyuan_1',
# 'double_990_close_company',
# 'double_beijing',
# 'double_beijing_1',
'single_28_huaxia',
# 'single_28_huaxia_2',
# 'single_897_yinchuan',
# 'single_897_yinchuan_2',
# 'single_1000_beijng_shoudu',
# 'single_1000_guangzhjou',
# 'single_1000_wuhan',
)
video_dir_pass=[
'single_1000_beijng_shoudu_kuan',
'single_1000_wuhan_kuan', ]
# video_path='/home/lishuang/Disk/shengshi_data/video_test_split_all/single_1000_beijng_shoudu_test_frame'
video_dir_path='/home/lishuang/Disk/shengshi_data/video_test_split_all'
video_paths=os.listdir(video_dir_path)
for video_dir in video_paths:
if video_dir not in videopath_list:
print(video_dir," pass")
continue
video_path=os.path.join(video_dir_path,video_dir)
# if video_dir !='double_54_zhuhai':
# continue
csv_path = os.path.join(video_dir_path, 'video_test_csv', f'{video_dir}_video_cut.csv')
# csv_path = os.path.join(os.path.join(videopath, ".."), f'{basedirname}_video_cut.csv')
video_name = []
video_name_dic = {}
with open(csv_path) as f:
lines = f.readlines()[1:]
for line in lines:
line = line.rstrip()
items = line.split(',')
video_name.append(items[1])
video_name_dic[items[1]] = [items[2], items[3], items[4], items[5]]
if os.path.isdir(video_path):
video_files=os.listdir(video_path)
alarmvideo_list = {}
for video_file in video_files:
if video_file!='616643FEF1380C0E_2019-10-19-11-37-49-812_passenger_00000061_2.mp4':
continue
if video_file[:-4] not in video_name_dic:
continue
videosource=os.path.join(video_path,video_file)
# if len(os.listdir(videosource))==0:
# continue
save_img = True
view_img = True
videodataset = LoadImages(videosource, img_size=imgsz)
video_file, extension = os.path.splitext(video_file)
alarmvideo_list[video_file] = 0
frame_record = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
frame_num=0
outvideo=str(Path(out) /video_dir/video_file)
x1t, y1t, x2t, y2t = video_name_dic[video_file]
ratio_width=1
ratio_height=1
x1t = int(x1t) * ratio_width
x2t = int(x2t) * ratio_width
y1t = int(y1t) * ratio_height
y2t = int(y2t) * ratio_height
if os.path.exists(outvideo):
shutil.rmtree(outvideo) # delete output folder
os.makedirs(outvideo) # make new output folder
for path, img, im0s, vid_cap in videodataset: #one video
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
boxnum = 0
boxnumbody = 0
boxnumhead = 0
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) /video_dir/ Path(p).name)
# txt_path = str(Path(out) /video_dir/video_file/ Path(p).stem) + ('_%g' % videodataset.frame if videodataset.mode == 'video' else '')
txt_path = str(Path(out) / video_dir / video_file /str(videodataset.frame))
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in det:
# if cls == 0:
# # label = 'person'
# boxnum += 1
# boxnumbody += 1
# elif cls == 1:
# # label = 'head'
# boxnumhead += 1
# if point_in_box(box_center, [x1, y1, x2, y2]):
# boxnumhead += 1 * person_result['class'] == 2
# boxnumbody += 1 * person_result['class'] == 1
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
with open(txt_path + '.txt', 'a') as f:
x0, y0, w0, h0 = xywh
h, w = im0.shape[:2]
x0 *= w
y0 *= h
w0 *= w
h0 *= h
x1 = x0 - w0 / 2
y1 = y0 - h0 / 2
if point_in_box([x0,y0], [x1t, y1t, x2t, y2t]):
boxnumhead += 1 * cls == 1
boxnumbody += 1 * cls == 0
f.write(('%s ' + '%.2g ' + '%d ' * 3 + '%d' + '\n') % (
names[int(cls)], conf, x1, y1, w0, h0)) # label format
# f.write(('%ss '+'%.2g ' * 5 + '\n') % (names[int(cls)], conf,*xywh)) # label format
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if videodataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer.write(im0)
image_path=os.path.join(outvideo,str(videodataset.frame)+'.jpg')
cv2.imwrite(image_path, im0)
if boxnumbody > 1 or boxnumhead > 1:
frame_record[frame_num % 10] = 1
else:
frame_record[frame_num % 10] = 0
frame_num+=1
if alarmvideo_list[video_file] ==0 and sum(frame_record) >7:
alarmvideo_list[video_file] =1
image_path = os.path.join(outvideo, str(videodataset.frame) + '_alarmvideo.jpg')
cv2.imwrite(image_path, im0)
file_data=""
for single_video in alarmvideo_list:
file_data += str(single_video) + ', value: ' + str(alarmvideo_list[single_video]) + '\n'
with open(f'{os.path.basename(video_path)}_video_result_{opt.conf_thres}.txt', 'a') as f:
f.write(file_data)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='/home/lishuang/Disk/remote/pycharm/yolov5/runs/last_s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='inference/output_video', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
detect()
create_pretrained(opt.weights, opt.weights)
else:
detect()