Create detect.py

detect.py

@@ -0,0 +1,186 @@
+import argparse
+import os
+import platform
+import shutil
+import time
+from pathlib import Path
+
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+from numpy import random
+
+from utils.google_utils import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import (
+    check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, strip_optimizer)
+from utils.plots import plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized
+
+from models.models import *
+from utils.datasets import *
+from utils.general import *
+
+def load_classes(path):
+    # Loads *.names file at 'path'
+    with open(path, 'r') as f:
+        names = f.read().split('\n')
+    return list(filter(None, names))  # filter removes empty strings (such as last line)
+
+def detect(save_img=False):
+    out, source, weights, view_img, save_txt, imgsz, cfg, names = \
+        opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.cfg, opt.names
+    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
+
+    # Initialize
+    device = select_device(opt.device)
+    if os.path.exists(out):
+        shutil.rmtree(out)  # delete output folder
+    os.makedirs(out)  # make new output folder
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+
+    # Load model
+    model = Darknet(cfg, imgsz).cuda()
+    model.load_state_dict(torch.load(weights[0], map_location=device)['model'])
+    #model = attempt_load(weights, map_location=device)  # load FP32 model
+    #imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
+    model.to(device).eval()
+    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'])  # load weights
+        modelc.to(device).eval()
+
+    # Set Dataloader
+    vid_path, vid_writer = None, None
+    if webcam:
+        view_img = True
+        cudnn.benchmark = True  # set True to speed up constant image size inference
+        dataset = LoadStreams(source, img_size=imgsz)
+    else:
+        save_img = True
+        dataset = LoadImages(source, img_size=imgsz, auto_size=64)
+
+    # Get names and colors
+    names = load_classes(names)
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
+
+    # Run inference
+    t0 = time.time()
+    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
+    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
+    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 = path[i], '%g: ' % i, im0s[i].copy()
+            else:
+                p, s, im0 = path, '', im0s
+
+            save_path = str(Path(out) / Path(p).name)
+            txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
+            s += '%gx%g ' % img.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            if det is not None and 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 += '%g %ss, ' % (n, names[int(c)])  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in det:
+                    if save_txt:  # Write to file
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        with open(txt_path + '.txt', 'a') as f:
+                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+
+                    if save_img or view_img:  # Add bbox to image
+                        label = '%s %.2f' % (names[int(cls)], conf)
+                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
+
+            # Print time (inference + NMS)
+            print('%sDone. (%.3fs)' % (s, t2 - t1))
+
+            # Stream results
+            if view_img:
+                cv2.imshow(p, im0)
+                if cv2.waitKey(1) == ord('q'):  # q to quit
+                    raise StopIteration
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'images':
+                    cv2.imwrite(save_path, im0)
+                else:
+                    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:
+        print('Results saved to %s' % Path(out))
+        if platform == 'darwin' and not opt.update:  # MacOS
+            os.system('open ' + save_path)
+
+    print('Done. (%.3fs)' % (time.time() - t0))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default='yolor_p6.pt', help='model.pt path(s)')
+    parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam
+    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
+    parser.add_argument('--img-size', type=int, default=1280, help='inference size (pixels)')
+    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.5, 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('--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('--cfg', type=str, default='cfg/yolor_p6.cfg', help='*.cfg path')
+    parser.add_argument('--names', type=str, default='data/coco.names', help='*.cfg path')
+    opt = parser.parse_args()
+    print(opt)
+
+    with torch.no_grad():
+        if opt.update:  # update all models (to fix SourceChangeWarning)
+            for opt.weights in ['']:
+                detect()
+                strip_optimizer(opt.weights)
+        else:
+            detect()