mirror of https://github.com/WongKinYiu/yolov7.git
263 lines
14 KiB
Python
263 lines
14 KiB
Python
import argparse
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import copy
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import time
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from pathlib import Path
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import cv2
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import torch
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import torch.backends.cudnn as cudnn
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from numpy import random
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import os
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import tifffile
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import copy
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import numpy as np
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from models.experimental import attempt_load
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from utils.datasets import LoadStreams, LoadImages, scaling_image
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from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
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scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
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from utils.plots import plot_one_box
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from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
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def detect(save_img=False):
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source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
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save_img = not opt.nosave #and not source.endswith('.txt') # save inference images
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webcam = source.isnumeric() or source.lower().startswith( # removed HK or source.endswith('.txt')
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('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = webcam and not source.endswith('.txt')
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# Directories
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save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Initialize
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set_logging()
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device = select_device(opt.device)
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half = device.type != 'cpu' # half precision only supported on CUDA
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# Load model
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model = attempt_load(weights, map_location=device) # load FP32 model
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stride = int(model.stride.max()) # model stride
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imgsz = check_img_size(imgsz, s=stride) # check img_size
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if trace:
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model = TracedModel(model, device, opt.img_size, opt.input_channels)
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if half:
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model.half() # to FP16
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# Second-stage classifier
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classify = False
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if classify:
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modelc = load_classifier(name='resnet101', n=2) # initialize
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modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
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# Set Dataloader
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vid_path, vid_writer = None, None
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride)
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride,
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scaling_type=opt.norm_type, input_channels=opt.input_channels,
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no_tir_signal=opt.no_tir_signal,
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tir_channel_expansion=opt.tir_channel_expansion,
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rel_path_for_list_files=opt.rel_path_for_list_files)
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# Get names and colors
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names = model.module.names if hasattr(model, 'module') else model.names
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
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# Run inference
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if device.type != 'cpu':
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model(torch.zeros(1, opt.input_channels, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
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old_img_w = old_img_h = imgsz
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old_img_b = 1
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t0 = time.time()
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for path, img, im0s, vid_cap in dataset:
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if os.path.basename(path).split('.')[1] == 'tiff': # im0s only for plotting version
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im0s = np.repeat(im0s[ :, :, np.newaxis], 3, axis=2) # convert GL to RGB by replication
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im0s = scaling_image(im0s, scaling_type=opt.norm_type)
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if im0s.max()<=1:
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im0s = im0s*255
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# im0s = copy.deepcopy(np.uint8(img.transpose(1,2,0) * 255.0))
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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# img /= 255.0 # 0 - 255 to 0.0 - 1.0
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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# Warmup
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if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
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old_img_b = img.shape[0]
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old_img_h = img.shape[2]
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old_img_w = img.shape[3]
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for i in range(3):
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model(img, augment=opt.augment)[0]
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# Inference
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t1 = time_synchronized()
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with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
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pred = model(img, augment=opt.augment)[0]
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t2 = time_synchronized()
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# Apply NMS
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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t3 = time_synchronized()
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# Apply Classifier
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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# Process detections
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for i, det in enumerate(pred): # detections per image
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if webcam: # batch_size >= 1
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p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
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else:
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p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # img.jpg
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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# Write results
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for *xyxy, conf, cls in reversed(det):
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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if save_img or view_img: # Add bbox to image
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label = f'{names[int(cls)]} {conf:.2f}'
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
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# Print time (inference + NMS)
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print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
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# Stream results
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if view_img:
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cv2.imshow(str(p), im0)
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cv2.waitKey(1) # 1 millisecond
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'image':
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print(save_path,os.path.basename(save_path).split('.'))
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if os.path.basename(save_path).split('.')[1] == 'tiff':
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#print('ka')
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save_path = os.path.join(os.path.dirname(save_path), os.path.basename(save_path).split('.')[0] + '.png')
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cv2.imwrite(save_path, im0)
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else:
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cv2.imwrite(save_path, im0)
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print(f" The image with the result is saved in: {save_path}")
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else: # 'video' or 'stream'
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if vid_path != save_path: # new video
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vid_path = save_path
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if isinstance(vid_writer, cv2.VideoWriter):
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vid_writer.release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path += '.mp4'
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer.write(im0)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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#print(f"Results saved to {save_dir}{s}")
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print(f'Done. ({time.time() - t0:.3f}s)')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
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parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
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parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--update', action='store_true', help='update all models')
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parser.add_argument('--project', default='runs/detect', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
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parser.add_argument('--norm-type', type=str, default='standardization',
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choices=['standardization', 'single_image_0_to_1', 'single_image_mean_std','single_image_percentile_0_255',
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'single_image_percentile_0_1', 'remove+global_outlier_0_1'],
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help='Normalization approach')
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parser.add_argument('--no-tir-signal', action='store_true', help='')
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parser.add_argument('--tir-channel-expansion', action='store_true', help='drc_per_ch_percentile')
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parser.add_argument('--input-channels', type=int, default=1, help='')
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parser.add_argument('--save-path', default='', help='save to project/name')
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parser.add_argument('--rel-path-for-list-files', default='/mnt/Data/hanoch/tir_frames_rois/yolo7_tir_data_all', help='')
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opt = parser.parse_args()
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if opt.tir_channel_expansion: # operates over 3 channels
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opt.input_channels = 3
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if opt.tir_channel_expansion and opt.norm_type != 'single_image_percentile_0_1': # operates over 3 channels
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print('Not a good combination')
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print(opt)
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#check_requirements(exclude=('pycocotools', 'thop'))
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with torch.no_grad():
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if opt.update: # update all models (to fix SourceChangeWarning)
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for opt.weights in ['yolov7.pt']:
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detect()
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strip_optimizer(opt.weights)
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else:
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detect()
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# --source yolov7/tir_od/fog_data_set.txt
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"""
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python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
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python -u ./yolov7/detect.py --weights ./yolov7/yolov7.pt --conf 0.25 --img-size 640 --device 0 --save-txt --source /home/hanoch/projects/tir_frames_rois/png/Rotem_test_22c_dec18.png
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python -u ./yolov7/detect.py --weights ./yolov7/yolov7.pt --conf 0.25 --img-size 640 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_frames_rois/yolo7_tir_data_all/TIR10_v20_Dec18_Test22C_20181127_223533_FS_210F_0001_5500_ROTEM_left_roi_220_4707.tiff
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--weights ./yolov7/yolov7.pt --conf 0.25 --img-size 640 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_frames_rois/yolo7_tir_data_all/TIR10_v20_Dec18_Test22C_20181127_223533_FS_210F_0001_5500_ROTEM_left_roi_220_4707.tiff
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--weights ./yolov7/yolov7.pt --conf 0.25 --img-size 640 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_frames_rois/yolo7_tir_data_all/TIR10_V50_OCT21_Test46A_ML_RD_IL_2021_08_05_14_48_05_FS_210_XGA_630_922_DENIS_right_roi_210_881.tiff
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--weights ./yolov7/yolov7.pt --conf 0.25 --img-size 640 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_frames_rois/yolo7_tir_data_all/TIR135_V80_JUL23_Test55A_SY_RD_US_2023_01_18_07_29_38_FS_50_XGA_0001_3562_Shahar_left_roi_50_1348.tiff
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--weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.pt --conf 0.25 --img-size 640 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_frames_rois/fog/28_02_2019_16_05_01[1]_04783.tiff
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YOLO model
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--weights ./yolov7/yolov7.pt --conf 0.25 --img-size 640 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_od/Snipaste_2024-09-15_09-00-58_tir_135_TIR135_V80_JUL23_Test55A_SY_RD_US_2023_01_18_07_29_38_FS_50_XGA_0001_3562_Shahar_left_roi_50_1348.png
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--weights /mnt/Data/hanoch/runs/train/yolov7575/weights/best.pt --conf 0.01 --img-size 640 --input-channels 1 --device 0 --save-txt --norm-type single_image_percentile_0_1 --source /home/hanoch/projects/tir_frames_rois/tir_tiff_tiff_files/TIR8_V50_Test19G_Jul20_2018-12-06_13-39-17_FS_50F_0114_6368_ROTEM_right_roi_50_345.tiff
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""" |