From bfe6d560c0f8c923b349f98adb9ddfd0d40bc040 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 2 Sep 2019 16:04:54 +0200 Subject: [PATCH] updates Signed-off-by: Glenn Jocher --- detect.py | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/detect.py b/detect.py index 56194118..2f3fb940 100644 --- a/detect.py +++ b/detect.py @@ -2,7 +2,7 @@ import argparse import time from sys import platform -from models import * +from models import * # set ONNX_EXPORT in models.py from utils.datasets import * from utils.utils import * @@ -14,7 +14,6 @@ def detect(save_txt=False, save_img=True, stream_img=False): # Initialize device = torch_utils.select_device(force_cpu=ONNX_EXPORT) - torch.backends.cudnn.benchmark = False # set False to speed up variable image size inference if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder @@ -30,6 +29,7 @@ def detect(save_txt=False, save_img=True, stream_img=False): # Fuse Conv2d + BatchNorm2d layers # model.fuse() + # torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference # Eval mode model.to(device).eval() @@ -50,9 +50,9 @@ def detect(save_txt=False, save_img=True, stream_img=False): if webcam: save_img = False stream_img = True - dataloader = LoadWebcam(opt.source, img_size=img_size, half=opt.half) + dataset = LoadWebcam(opt.source, img_size=img_size, half=opt.half) else: - dataloader = LoadImages(opt.source, img_size=img_size, half=opt.half) + dataset = LoadImages(opt.source, img_size=img_size, half=opt.half) # Get classes and colors classes = load_classes(parse_data_cfg(opt.data)['names']) @@ -60,7 +60,7 @@ def detect(save_txt=False, save_img=True, stream_img=False): # Run inference t0 = time.time() - for path, img, im0, vid_cap in dataloader: + for path, img, im0, vid_cap in dataset: t = time.time() save_path = str(Path(out) / Path(path).name) @@ -69,15 +69,15 @@ def detect(save_txt=False, save_img=True, stream_img=False): pred, _ = model(img) det = non_max_suppression(pred.float(), opt.conf_thres, opt.nms_thres)[0] + s = '%gx%g ' % img.shape[2:] # string to print image size 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 to screen - print('%gx%g ' % img.shape[2:], end='') # print image size + # Print results for c in det[:, -1].unique(): - n = (det[:, -1] == c).sum() - print('%g %ss' % (n, classes[int(c)]), end=', ') + n = (det[:, -1] == c).sum() # detections per class + s += '%g %ss' % (n, classes[int(c)]) # add to string # Write results for *xyxy, conf, _, cls in det: @@ -89,13 +89,15 @@ def detect(save_txt=False, save_img=True, stream_img=False): label = '%s %.2f' % (classes[int(cls)], conf) plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) - print('Done. (%.3fs)' % (time.time() - t)) + print('%sDone. (%.3fs)' % (s, time.time() - t)) - if stream_img: # Stream results + # Stream results + if stream_img: cv2.imshow(opt.weights, im0) - if save_img: # Save image with detections - if dataloader.mode == 'images': + # 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