#print('-->start anju_detect')

#source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size

agnostic_nms=False

augment=False

classes=None

conf_thres=0.25

device=''

exist_ok=True

imgsz=640

iou_thres=0.45

name='result_img'

nosave=False

project='static'

save_conf=False

save_txt=True

source = 'uploads/' + file_name

update=False

view_img=True

weights='best.pt'

save_img = True

# Directories

#print('-->detect')

save_dir = Path(increment_path(Path(project) / name, exist_ok=True)) # increment run

# Initialize

#print('-->Initialize')

set_logging()

device = select_device(device)

half = device.type != 'cpu' # half precision only supported on CUDA

# Load model

#print('-->Load model')

model = attempt_load(weights, map_location=device) # load FP32 model

stride = int(model.stride.max()) # model stride

imgsz = check_img_size(imgsz, s=stride) # check img_size

if half:

model.half() # to FP16

# Second-stage classifier

print('-->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']).to(device).eval()

# Set Dataloader

print('-->Set Dataloader')

vid_path, vid_writer = None, None

#print('-->3 source:',source)

#print('-->3 file_name:',file_name)

#print('-->4 source:',source)

dataset = LoadImages(source, img_size=imgsz, stride=stride)

# Get names and colors

print('-->Get names and colors')

names = model.module.names if hasattr(model, 'module') else model.names

colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

# Run inference

print('-->Run inference')

if device.type != 'cpu':

model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once

t0 = time.time()

labels = []

for path, img, im0s, vid_cap in dataset:

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

print('-->Inference')

t1 = time_synchronized()

pred = model(img, augment=augment)[0]

# Apply NMS

print('-->Apply NMS')

pred = non_max_suppression(pred, conf_thres, iou_thres, classes=classes, agnostic=agnostic_nms)

t2 = time_synchronized()

# Apply Classifier

print('-->Apply Classifier')

if classify:

pred = apply_classifier(pred, modelc, img, im0s)

# Process detections

print('-->Process detections')

for i, det in enumerate(pred): # detections per image

p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

p = Path(p) # to Path

save_path = str(save_dir / p.name) # img.jpg

txt_path = str(save_dir / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt

s += '%gx%g ' % img.shape[2:] # print string

gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh

# ysc 20210420 delete old file , add labe list

if os.path.isfile(txt_path + '.txt'):

os.remove(txt_path + '.txt')

if len(det):

# Rescale boxes from img_size to im0 size

det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

# Print results

print('-->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

# Write results

print('-->Write results')

for *xyxy, conf, cls in reversed(det):

if save_txt: # Write to file

#20210420 ysc save label name

#xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh

#line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format

label = f'{names[int(cls)]} {conf:.2f}' # detected label name

with open(txt_path + '.txt', 'a') as f:

#f.write(('%g ' * len(line)).rstrip() % line + '\n') # print labe map

f.write(label + '\n')

if save_img or view_img: # Add bbox to image

label = f'{names[int(cls)]}-{conf:.2f}'

plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

print('-->label :' ,label)

labels.append(label)

# Print time (inference + NMS)

print(f'{s}Done. ({t2 - t1:.3f}s)')

# Save results (image with detections)

print('-->Save results (image with detections)')

if save_img:

cv2.imwrite(save_path, im0)

print('labels=',labels)

if save_txt or save_img:

s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''

print(f"Results saved to {save_dir}{s}")

#print(f'Done. ({time.time() - t0:.3f}s)')