def rtsp_to_mongodb():
with open("/home/asyed/airflow/dags/parameters.json") as f:
parms = json.load(f)
agnostic_nms = parms["agnostic_nms"]
augment = parms["augment"]
classes = parms["classes"]
conf_thres = parms["conf_thres"]
config_deepsort = parms["config_deepsort"]
deep_sort_model = parms["deep_sort_model"]
device = parms["device"]
dnn = False
evaluate = parms["evaluate"]
exist_ok = parms["exist_ok"]
fourcc = parms["fourcc"]
half = False
print(device)
imgsz = parms["imgsz"]
iou_thres = parms["iou_thres"]
max_det = parms["max_det"]
name = parms["name"]
# save_vid = parms["save_vid"]
#show_vid = parms["show_vid"]
source = parms["source"]
visualize = parms["visualize"]
yolo_model = parms["yolo_model"]
webcam = parms["webcam"]
save_txt = parms["save_txt"]
homography = np.array(parms["homography"])
url = "mongodb://localhost:27017"
client = MongoClient(url)
db = client.trajectory_database
today_date = date.today().strftime("%m-%d-%y")
new = "file_image_coordinates_" + today_date
collection = db[new]
cfg = get_config()
cfg.merge_from_file(config_deepsort)
deepsort = DeepSort(deep_sort_model,
max_dist=cfg.DEEPSORT.MAX_DIST,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE,
n_init=cfg.DEEPSORT.N_INIT,
nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
device = select_device(device)
half &= device.type != 'cpu' # half precision only supported on CUDA
# The MOT16 evaluation runs multiple inference streams in parallel, each one writing to
# its own .txt file. Hence, in that case, the output folder is not restored
# make new output folder
# Load model
device = select_device(device)
model = DetectMultiBackend(yolo_model, device=device, dnn=dnn)
stride, names, pt, jit, _ = model.stride, model.names, model.pt, model.jit, model.onnx
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
if pt:
model.model.half() if half else model.model.float()
# Set Dataloader
vid_path, vid_writer = None, None
# Check if environment supports image displays
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source,
img_size=imgsz,
stride=stride,
auto=pt and not jit)
bs = len(dataset) # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
if pt and device.type != 'cpu':
model(
torch.zeros(1, 3, *imgsz).to(device).type_as(
next(model.model.parameters()))) # warmup
# global framess_im2
dt, seen = [0.0, 0.0, 0.0, 0.0], 0
# arr = None
past = []
for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset):
t1 = time_sync()
img = torch.from_numpy(img).to(device)
# print("raw_frame",img.shape)
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)
t2 = time_sync()
dt[0] += t2 - t1
pred = model(img, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
pred = non_max_suppression(pred,
conf_thres,
iou_thres,
classes,
agnostic_nms,
max_det=max_det)
dt[2] += time_sync() - t3
# Process detections
# dets_per_img = []
for i, det in enumerate(pred): # detections per image
seen += 1
if webcam: # batch_size >= 1
p, im0, _ = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
annotator = Annotator(im0, line_width=2, pil=not ascii)
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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
xywhs = xyxy2xywh(det[:, 0:4])
confs = det[:, 4]
clss = det[:, 5]
# pass detections to deepsort
t4 = time_sync()
outputs = deepsort.update(xywhs.cpu(), confs.cpu(),
clss.cpu(), im0)
t5 = time_sync()
dt[3] += t5 - t4
if len(outputs) > 0:
for j, (output, conf) in enumerate(zip(outputs,
confs)):
bboxes = output[0:4]
id = output[4]
cls = output[5]
c = int(cls) # integer class
label = f'{id} {names[c]} {conf:.2f}'
annotator.box_label(bboxes,
label,
color=colors(c, True))
if save_txt:
# to MOT format
bbox_left = output[0]
bbox_top = output[1]
bbox_w = output[2] - output[0]
bbox_h = output[3] - output[1]
# bbox_left = bbox_left + bbox_h
bbox_top = bbox_top + bbox_h
agent_data = {
'frame': int(frame_idx + 1),
'agent_id': int(id),
"labels": str(names[c]),
"x": int(bbox_left),
"y": int(bbox_top)
}
print("agent", agent_data)
collection.insert_one(agent_data)
#db.object_detection.insert_one(agent_data)
#db.pedestrian_detection_15_june.insert_one(agent_data)
#db.test_21_july.insert_one(agent_data)
LOGGER.info(
f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)'
)
else:
deepsort.increment_ages()
LOGGER.info('No detections')
im0 = annotator.result()
def detect(opt):
memory = {}
counter = 0
out, source, yolo_model, deep_sort_model, show_vid, save_vid, save_txt, imgsz, evaluate, half, project, name, exist_ok= \
opt.output, opt.source, opt.yolo_model, opt.deep_sort_model, opt.show_vid, opt.save_vid, \
opt.save_txt, opt.imgsz, opt.evaluate, opt.half, opt.project, opt.name, opt.exist_ok
webcam = source == '0' or source.startswith('rtsp') or source.startswith(
'http') or source.endswith('.txt')
# initialize deepsort
cfg = get_config()
cfg.merge_from_file(opt.config_deepsort)
deepsort = DeepSort(deep_sort_model,
torch.device("cpu"),
max_dist=cfg.DEEPSORT.MAX_DIST,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE,
n_init=cfg.DEEPSORT.N_INIT,
nn_budget=cfg.DEEPSORT.NN_BUDGET)
# Initialize
device = select_device(opt.device)
half &= device.type != 'cpu' # half precision only supported on CUDA
# The MOT16 evaluation runs multiple inference streams in parallel, each one writing to
# its own .txt file. Hence, in that case, the output folder is not restored
if not evaluate:
if os.path.exists(out):
pass
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Directories
save_dir = increment_path(Path(project) / name,
exist_ok=exist_ok) # increment run
save_dir.mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(yolo_model, device=device, dnn=opt.dnn)
stride, names, pt, jit, _ = model.stride, model.names, model.pt, model.jit, model.onnx
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
if pt:
model.model.half() if half else model.model.float()
# Set Dataloader
vid_path, vid_writer = None, None
# Check if environment supports image displays
if show_vid:
show_vid = check_imshow()
# Dataloader
if webcam:
show_vid = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source,
img_size=imgsz,
stride=stride,
auto=pt and not jit)
bs = len(dataset) # batch_size
else:
dataset = LoadImages(source,
img_size=imgsz,
stride=stride,
auto=pt and not jit)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
# extract what is in between the last '/' and last '.'
txt_file_name = source.split('/')[-1].split('.')[0]
txt_path = str(Path(save_dir)) + '/' + txt_file_name + '.txt'
if pt and device.type != 'cpu':
model(
torch.zeros(1, 3, *imgsz).to(device).type_as(
next(model.model.parameters()))) # warmup
dt, seen = [0.0, 0.0, 0.0, 0.0], 0
regionid = set()
for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset):
t1 = time_sync()
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)
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem,
mkdir=True) if opt.visualize else False
pred = model(img, augment=opt.augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
opt.classes,
opt.agnostic_nms,
max_det=opt.max_det)
dt[2] += time_sync() - t3
# Process detections
for i, det in enumerate(pred): # detections per image
seen += 1
if webcam: # batch_size >= 1
p, im0, _ = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...
s += '%gx%g ' % img.shape[2:] # print string
annotator = Annotator(im0,
line_width=2,
font='Arial.ttf',
pil=not ascii)
if det is not None and len(det):
tboxes = []
indexIDs = []
previous = memory.copy()
memory = {}
# 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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
xywhs = xyxy2xywh(det[:, 0:4])
confs = det[:, 4]
clss = det[:, 5]
# pass detections to deepsort
t4 = time_sync()
outputs = deepsort.update(xywhs.cpu(), confs.cpu(), clss.cpu(),
im0)
t5 = time_sync()
dt[3] += t5 - t4
# draw boxes for visualization
if len(outputs) > 0:
for j, (output, conf) in enumerate(zip(outputs, confs)):
bboxes = output[0:4]
id = output[4]
cls = output[5]
roi = [(0, 0), (640, 0), (640, 380), (0, 380)]
(x, y) = (int(bboxes[0]), int(bboxes[1]))
(w, h) = (int(bboxes[2]), int(bboxes[3]))
inside = cv2.pointPolygonTest(np.array(roi), (x, h),
False)
if inside > 0:
regionid.add(id)
c = int(cls) # integer class
label = f' {names[c]} {conf:.2f}'
cv2.putText(im0, "count =" + str(len(regionid)),
(20, 50), 0, 1, (100, 200, 0), 2)
annotator.box_label(bboxes,
label,
color=colors(c, True))
if save_txt:
# to MOT format
bbox_left = output[0]
bbox_top = output[1]
bbox_w = output[2] - output[0]
bbox_h = output[3] - output[1]
# Write MOT compliant results to file
with open(txt_path, 'a') as f:
f.write(('%g ' * 10 + '\n') % (
frame_idx + 1,
id,
bbox_left, # MOT format
bbox_top,
bbox_w,
bbox_h,
-1,
-1,
-1,
-1))
LOGGER.info(
f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)'
)
LOGGER.info(f'counter = {len(regionid)}')
else:
deepsort.increment_ages()
LOGGER.info('No detections')
# Stream results
im0 = annotator.result()
if show_vid:
cv2.imshow(str(p), im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_vid:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
if vid_cap: # video
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))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps,
(w, h))
vid_writer.write(im0)
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(
f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms deep sort update \
per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_vid:
print('Results saved to %s' % save_path)
if platform == 'darwin': # MacOS
os.system('open ' + save_path)