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| import torch | |
| import argparse | |
| import gradio as gr | |
| from PIL import Image | |
| from numpy import random | |
| from pathlib import Path | |
| import os | |
| import time | |
| import torch.backends.cudnn as cudnn | |
| from models.experimental import attempt_load | |
| import cv2 | |
| 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, TracedModel | |
| os.system('git clone https://github.com/WongKinYiu/yolov7') | |
| #model='best' | |
| def Custom_detect(img,mode): | |
| if mode=='Yolov7': | |
| model='best' | |
| if mode=='Yolov7_tiny': | |
| model='tiny' | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)') | |
| parser.add_argument('--source', type=str, default='Temp_files/', help='source') | |
| 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('--nosave', action='store_true', help='do not save images/videos') | |
| 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') | |
| parser.add_argument('--trace', action='store_true', help='trace model') | |
| opt = parser.parse_args() | |
| img.save("Temp_files/test.jpg") | |
| source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.trace | |
| save_img = True | |
| webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) | |
| save_dir = Path(increment_path(Path(opt.project)/opt.name,exist_ok=opt.exist_ok)) | |
| (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) | |
| set_logging() | |
| device = select_device(opt.device) | |
| half = device.type != 'cpu' | |
| model = attempt_load(weights, map_location=device) | |
| stride = int(model.stride.max()) | |
| imgsz = check_img_size(imgsz, s=stride) | |
| if trace: | |
| model = TracedModel(model, device, opt.img_size) | |
| if half: | |
| model.half() | |
| 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() | |
| vid_path, vid_writer = None, None | |
| if webcam: | |
| view_img = check_imshow() | |
| cudnn.benchmark = True | |
| dataset = LoadStreams(source, img_size=imgsz, stride=stride) | |
| else: | |
| dataset = LoadImages(source, img_size=imgsz, stride=stride) | |
| names = model.module.names if hasattr(model, 'module') else model.names | |
| colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] | |
| if device.type != 'cpu': | |
| model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) | |
| 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() | |
| img /= 255.0 | |
| if img.ndimension() == 3: | |
| img = img.unsqueeze(0) | |
| # Inference | |
| t1 = time_synchronized() | |
| pred = model(img, augment=opt.augment)[0] | |
| 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) | |
| for i, det in enumerate(pred): | |
| if webcam: | |
| 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) | |
| save_path = str(save_dir / p.name) | |
| txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt | |
| s += '%gx%g ' % img.shape[2:] | |
| gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] | |
| if len(det): | |
| det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() | |
| for c in det[:, -1].unique(): | |
| n = (det[:, -1] == c).sum() | |
| s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " | |
| for *xyxy, conf, cls in reversed(det): | |
| if save_txt: | |
| xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() | |
| line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) | |
| with open(txt_path + '.txt', 'a') as f: | |
| f.write(('%g ' * len(line)).rstrip() % line + '\n') | |
| if save_img or view_img: | |
| label = f'{names[int(cls)]} {conf:.2f}' | |
| plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=2) | |
| if view_img: | |
| cv2.imshow(str(p), im0) | |
| cv2.waitKey(1) | |
| if save_img: | |
| if dataset.mode == 'image': | |
| cv2.imwrite(save_path, im0) | |
| else: | |
| if vid_path != save_path: | |
| vid_path = save_path | |
| if isinstance(vid_writer, cv2.VideoWriter): | |
| vid_writer.release() | |
| if vid_cap: | |
| 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)) | |
| else: | |
| fps, w, h = 30, im0.shape[1], im0.shape[0] | |
| save_path += '.mp4' | |
| vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 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'Done. ({time.time() - t0:.3f}s)') | |
| return Image.fromarray(im0[:,:,::-1]) | |
| inp = gr.Image(type="pil") | |
| #"Custom-Detection","Yolov7-model-detection" | |
| inp2= gr.Dropdown(choices=['Yolov7','Yolov7_tiny']) | |
| output = gr.Image(type="pil") | |
| examples=[["Examples/Image1.jpg","Image1"],["Examples/Image2.jpg","Image2"]] | |
| io=gr.Interface(fn=Custom_detect, inputs=[inp,inp2], outputs=output, title='Port Structures Detection With Custom YOLOv7',description="classes: container, crane, forklift, person, ship, stacker, truck",examples=examples,cache_examples=False) | |
| io.launch() | |