Merge branch 'it17167710_prathapa' into 'master'

It17167710 prathapa

See merge request !32

Child_accident_detection/Child_boundary_breach/Child_boundry_Breach.py

+
+import time
+from pathlib import Path
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+from models.experimental import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
+    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
+from utils.plots import colors, plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized
+from children_and_door import data_streem
+
+
+def detect(weights='mydataset.pt',  # model.pt path(s)
+           source='Door_vids/chi4.mp4',  # file/dir/URL/glob, 0 for webcam
+           imgsz=640,  # inference size (pixels)
+           conf_thres=0.25,  # confidence threshold
+           iou_thres=0.35,  # NMS IOU threshold
+           max_det=1000,  # maximum detections per image
+           device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+           view_img= False,  # show results
+           save_txt=False,  # save results to *.txt
+           save_conf=False,  # save confidences in --save-txt labels
+           save_crop=False,  # save cropped prediction boxes
+           nosave=False,  # do not save images/videos
+           classes=None,  # filter by class: --class 0, or --class 0 2 3
+           agnostic_nms=False,  # class-agnostic NMS
+           augment=False,  # augmented inference
+           update=False,  # update all models
+           project='runs/detect',  # save results to project/name
+           name='exp',  # save results to project/name
+           exist_ok=False,  # existing project/name ok, do not increment
+           line_thickness=3,  # bounding box thickness (pixels)
+           hide_labels=False,  # hide labels
+           hide_conf=False,  # hide confidences
+           half=False,  # use FP16 half-precision inference
+           ):
+
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+        ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=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(device)
+    half &= 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 image 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
+        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=augment)[0]
+
+        # Apply NMS
+        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+        t2 = time_synchronized()
+
+        # Apply Classifier
+        if classify:
+            pred = apply_classifier(pred, modelc, img, im0s)
+
+        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)
+
+            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 save_crop else im0  # for save_crop
+            if len(det):
+                object_frame = []
+
+                # Rescale boxes from img_size to im0 sizedg
+                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 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 save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+
+                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
+                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
+
+                        c1, c2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
+                        object_frame.append([str(names[c]),c1,c2])
+
+                        if save_crop:
+                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+
+
+            data_streem(object_frame)
+
+            # Stream results
+            if True:
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'image':
+                    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))
+                            font = cv2.FONT_HERSHEY_COMPLEX_SMALL
+                            cv2.putText(frame, "hiii", (10, 40 - 20), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
+                        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 ''
+
+    if update:
+        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
+
+
+
+if __name__ == '__main__':
+    detect()