in detect.py [0:0]
def detect(opt):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
save_img = not opt.nosave and (not source.endswith('.txt') or True) # save inference images
webcam = source.isnumeric() or (source.endswith('.txt') and False) or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
# Directories
save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = opt.half and device.type != 'cpu' # half precision only supported on CUDA
# 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
names = model.module.names if hasattr(model, 'module') else model.names # get class names
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']).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# 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()
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
if img.ndimension() == 3:
img = img.unsqueeze(0)
# img /= 255.0 # 0-255 to 0.0-1.0
img = norm_imgs(img, model)
# Inference
t1 = time_synchronized()
(pred, p_det), masks = model(img, augment=opt.augment)
masks = masks[0].sigmoid()
if opt.view_center:
masks = ((masks == F.max_pool2d(masks, 3, stride=1, padding=1)) & (masks > 0.3)).float()
clusters = p_det[1][0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms,
max_det=opt.max_det)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
image_name = osp.basename(p).split('.')[0]
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / 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
imc = im0.copy() if opt.save_crop else im0 # for opt.save_crop
if opt.view_cluster:
# cv2.imwrite(f'{save_dir}/{image_name}_0_raw.jpg', im0)
heatmap = (masks[i, 0].cpu().numpy() * 255.).astype(np.uint8)
heatmap = cv2.resize(heatmap, (im0.shape[1], im0.shape[0]), cv2.INTER_CUBIC)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_RAINBOW)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
image_att = cv2.addWeighted(im0, 0.4, heatmap, 0.5, 0)
cv2.imwrite(f'{save_dir}/{image_name}_1_attn.jpg', image_att)
label_path = str(p).replace('images', 'labels').replace('.jpg', '.txt')
if osp.exists(label_path):
with open(label_path, 'r') as f:
lines = f.read().splitlines()
gt_bboxes = [list(map(float, line.split())) for line in lines]
im1 = im0.copy()
for ci, xc, yc, w, h in gt_bboxes:
c = int(ci)
label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]}')
xyxy = [(xc - w / 2.) * im0.shape[1], (yc - h / 2.) * im0.shape[0],
(xc + w / 2.) * im0.shape[1], (yc + h / 2.) * im0.shape[0]]
plot_one_box(xyxy, im1, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
cv2.imwrite(f'{save_dir}/{image_name}_5_gt.jpg', im1)
# targets = torch.cat((torch.ones((len(gt_bboxes), 1)), torch.tensor(gt_bboxes)), dim=1)
# gt_mask = target2mask(targets, (3, *im0.shape[:2]), nc=1, stride=1)[0]
gt_mask_path = str(p).replace('/images/', '/masks/').replace('_masked.', '.').replace('.jpg', '.npy')
if os.path.exists(gt_mask_path):
gt_mask = np.load(gt_mask_path)
gt_mask = gt_mask[..., :1]
# gt_mask = gt_mask[..., 1:] / gt_mask[..., 1:].max()
heatmap = (gt_mask * 255.).astype(np.uint8)
# heatmap = cv2.resize(heatmap, (im0.shape[1], im0.shape[0]), cv2.INTER_CUBIC)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_RAINBOW)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
image_att = cv2.addWeighted(im0, 0.4, heatmap, 0.5, 0)
cv2.imwrite(f'{save_dir}/{image_name}_2_attn_gt.jpg', image_att)
cluster = clusters[clusters[:, 0] == i, 1:] * 8
cluster = scale_coords(img.shape[2:], cluster, im0.shape).round()
im2 = im0.copy()
for ci, xyxy in enumerate(cluster):
# plot_one_box(xyxy, im0, color=(0, 255, 0), line_thickness=opt.line_thickness * 2)
x1, y1, x2, y2 = list(map(int, xyxy))
plot_one_box((x1, y1, x2, y2), im2, color=(0, 255, 0), line_thickness=opt.line_thickness * 2)
cv2.imwrite(f'{save_dir}/{image_name}_3_cluster.jpg', im2)
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
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
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
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
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or opt.save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
if opt.save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# Stream results
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
if opt.view_cluster:
cv2.imwrite(f'{save_dir}/{image_name}_4_pred.jpg', im0)
else:
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
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]
save_path += '.mp4'
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer.write(im0)
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)')