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detect.py

@@ -34,6 +34,7 @@
 import platform
 import sys
 from pathlib import Path
+
 import numpy as np
 import torch
 
@@ -47,54 +48,68 @@
 
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
-                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    non_max_suppression,
+    print_args,
+    scale_boxes,
+    strip_optimizer,
+    xyxy2xywh,
+)
 from utils.torch_utils import select_device, smart_inference_mode
 
 
 @smart_inference_mode()
 def run(
-        weights=ROOT / 'yolov5s.pt',  # model path or triton URL
-        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
-        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
-        imgsz=(640, 640),  # inference size (height, width)
-        conf_thres=0.25,  # confidence threshold
-        iou_thres=0.45,  # 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_csv=False,  # save results in CSV format
-        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
-        visualize=False,  # visualize features
-        update=False,  # update all models
-        project=ROOT / '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
-        dnn=False,  # use OpenCV DNN for ONNX inference
-        vid_stride=1,  # video frame-rate stride
+    weights=ROOT / "yolov5s.pt",  # model path or triton URL
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(640, 640),  # inference size (height, width)
+    conf_thres=0.25,  # confidence threshold
+    iou_thres=0.45,  # 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_csv=False,  # save results in CSV format
+    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
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "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
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
 ):
     source = str(source)
-    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    save_img = not nosave and not source.endswith(".txt")  # save inference images
     is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
-    webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
-    screenshot = source.lower().startswith('screen')
+    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
+    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+    screenshot = source.lower().startswith("screen")
     if is_url and is_file:
         source = check_file(source)  # download
 
     # 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
+    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
 
     # Load model
     device = select_device(device)
@@ -138,12 +153,12 @@ def run(
         # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
 
         # Define the path for the CSV file
-        csv_path = save_dir / 'predictions.csv'
+        csv_path = save_dir / "predictions.csv"
 
         # Create or append to the CSV file
         def write_to_csv(image_name, prediction, confidence):
-            data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence}
-            with open(csv_path, mode='a', newline='') as f:
+            data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
+            with open(csv_path, mode="a", newline="") as f:
                 writer = csv.DictWriter(f, fieldnames=data.keys())
                 if not csv_path.is_file():
                     writer.writeheader()
@@ -154,14 +169,14 @@ def write_to_csv(image_name, prediction, confidence):
             seen += 1
             if webcam:  # batch_size >= 1
                 p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f'{i}: '
+                s += f"{i}: "
             else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
 
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
-            s += '%gx%g ' % im.shape[2:]  # print string
+            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
+            s += "%gx%g " % im.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
             annotator = Annotator(im0, line_width=line_thickness, example=str(names))
@@ -179,15 +194,19 @@ def write_to_csv(image_name, prediction, confidence):
                 img_center = np.array([im0.shape[1] // 2, im0.shape[0] // 2])
 
                 # Calculate centers of all detection boxes and find the closest one to the image center
-                centers = np.array([[(xyxy[0].cpu() + xyxy[2].cpu()) / 2, (xyxy[1].cpu() + xyxy[3].cpu()) / 2] for *xyxy, _, _ in reversed(det)])
+                centers = np.array(
+                    [
+                        [(xyxy[0].cpu() + xyxy[2].cpu()) / 2, (xyxy[1].cpu() + xyxy[3].cpu()) / 2]
+                        for *xyxy, _, _ in reversed(det)
+                    ]
+                )
                 distances = np.linalg.norm(centers - img_center, axis=1)
                 closest_idx = np.argmin(distances)
 
                 # Draw boxes, marking the closest one in green
                 for j, (*xyxy, conf, cls) in enumerate(reversed(det)):
                     color = (0, 255, 0) if j == closest_idx else colors(int(cls), True)
-                    annotator.box_label(xyxy, f'{names[int(cls)]} {conf:.2f}', color=color)
-
+                    annotator.box_label(xyxy, f"{names[int(cls)]} {conf:.2f}", color=color)
 
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
@@ -200,30 +219,30 @@ def write_to_csv(image_name, prediction, confidence):
                 # Write results
                 for *xyxy, conf, cls in reversed(det):
                     c = int(cls)  # integer class
-                    label = names[c] if hide_conf else f'{names[c]}'
+                    label = names[c] if hide_conf else f"{names[c]}"
                     confidence = float(conf)
-                    confidence_str = f'{confidence:.2f}'
+                    confidence_str = f"{confidence:.2f}"
 
                     if save_csv:
                         write_to_csv(p.name, label, confidence_str)
 
                     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(f'{txt_path}.txt', 'a') as f:
-                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
+                        with open(f"{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}')
+                        label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
                         annotator.box_label(xyxy, label, color=colors(c, True))
                     if save_crop:
-                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+                        save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
 
             # Stream results
             im0 = annotator.result()
             if view_img:
-                if platform.system() == 'Linux' and p not in windows:
+                if platform.system() == "Linux" and p not in windows:
                     windows.append(p)
                     cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                     cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
@@ -232,7 +251,7 @@ def write_to_csv(image_name, prediction, confidence):
 
             # Save results (image with detections)
             if save_img:
-                if dataset.mode == 'image':
+                if dataset.mode == "image":
                     cv2.imwrite(save_path, im0)
                 else:  # 'video' or 'stream'
                     if vid_path[i] != save_path:  # new video
@@ -245,66 +264,67 @@ def write_to_csv(image_name, prediction, confidence):
                             h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                         else:  # stream
                             fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                        save_path = str(Path(save_path).with_suffix(".mp4"))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
         LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
 
     # Print results
-    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
-    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
     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 ''
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
         LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
     if update:
         strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
 
 
 def parse_opt():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL')
-    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
-    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
-    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='show results')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
-    parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')
-    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
-    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
-    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
-    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('--visualize', action='store_true', help='visualize features')
-    parser.add_argument('--update', action='store_true', help='update all models')
-    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
-    parser.add_argument('--name', default='exp', help='save results to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
-    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
-    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
-    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")
+    parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
+    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
+    parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
+    parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
+    parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
+    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="show results")
+    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+    parser.add_argument("--save-csv", action="store_true", help="save results in CSV format")
+    parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
+    parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
+    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
+    parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
+    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("--visualize", action="store_true", help="visualize features")
+    parser.add_argument("--update", action="store_true", help="update all models")
+    parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name")
+    parser.add_argument("--name", default="exp", help="save results to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
+    parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
+    parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
     print_args(vars(opt))
     return opt
 
 
 def main(opt):
-    check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
+    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
     run(**vars(opt))
 
+
 # python detect.py --weights runs/train/exp10/weights/best.pt --source project/test
 # python detect.py --weights runs/train/exp10/weights/best.pt --source project/test
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     opt = parse_opt()
     main(opt)