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丸投げな質問だったため詳細を追記しました！ご迷惑おかけします。。

2021/03/13 05:45

投稿

Pino02

スコア1

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+### 前提・実現したいこと
-YOLOv5を使ってWebCamでリアルタイムで認識はしてくれいるんですが、コンソール上にしか結果が表示されないのでそれをtxtファイルに出力したいです。
+コンソール上にしか結果が表示されないのでそれをtxtファイルに出力したいです。
+### 発生している問題・エラーメッセージ
+yolov5-masterというディレクトリにYOLOv5のファイルを解凍してあります。
+その中のdetect.pyを`python detect.py --source 0`で実行しました。
+そうするとコマンドプロントには下記のものはその一部ですが結果が返ってきます。
+ですがこれをtxtファイルに出力できなくて困っています。
+### 試したこと
+とりあえずprintしているところが118行目の
+```python
+print(f'{s}Done. ({t2 - t1:.3f}s)')
+```
+だと分かったのでこれを変数に格納して
+強引に結果を切り取ったりしてみました。
+```python
+result = f'{s}Done. ({t2 - t1:.3f}s)'
+result = result[14:]
+```
+### Code
+```Python
+detect.py
+import argparse
+import time
+from pathlib import Path
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+from numpy import random
+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
+from utils.plots import plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized
+def detect(save_img=False):
+    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
+    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+        ('rtsp://', 'rtmp://', 'http://'))
+    # Directories
+    save_dir = Path(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 = 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
+    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:
+        save_img = True
+        dataset = LoadImages(source, img_size=imgsz, stride=stride)
+    # 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
+    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=opt.augment)[0]
+        # Apply NMS
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+        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], '%g: ' % i, im0s[i].copy(), dataset.count
+            else:
+                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 / '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
+            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 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 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':
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video'
+                    if vid_path != save_path:  # new video
+                        vid_path = save_path
+                        if isinstance(vid_writer, cv2.VideoWriter):
+                            vid_writer.release()  # release previous video writer
+                        fourcc = 'mp4v'  # output video codec
+                        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))
+                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), 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)')
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
+    parser.add_argument('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcam
+    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
+    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='display results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default='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')
+    opt = parser.parse_args()
+    print(opt)
+    check_requirements()
+    with torch.no_grad():
+        if opt.update:  # update all models (to fix SourceChangeWarning)
+            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
+                detect()
+                strip_optimizer(opt.weights)
+        else:
+            detect()
+```
+```cmd
+0: 480x640 1 tv, Done. (0.219s)
+0: 480x640 1 tv, 1 book, Done. (0.224s)
+0: 480x640 1 person, Done. (0.215s)
+0: 480x640 Done. (0.217s)
+0: 480x640 Done. (0.212s)
+0: 480x640 1 tv, Done. (0.205s)
+0: 480x640 1 tie, Done. (0.208s)
+0: 480x640 Done. (0.211s)
+0: 480x640 1 person, Done. (0.219s)
+0: 480x640 Done. (0.204s)
+0: 480x640 1 chair, Done. (0.218s)
+0: 480x640 1 carrot, Done. (0.210s)
+0: 480x640 Done. (0.212s)
+0: 480x640 Done. (0.215s)
+0: 480x640 1 person, Done. (0.211s)
+0: 480x640 1 person, Done. (0.214s)
+0: 480x640 1 person, 1 tv, 1 laptop, Done. (0.208s)
+```