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') """ Ejemplo de uso: para la empresa GCC , la imagen debe contener cualquiera equipo contra acidentes personales * Cascos de Seguridad. ... * Tapones para oídos y Orejeras. ... * Lentes de Seguridad. ... * Respiradores. ... * Chaleco de Seguridad. ... * Guantes de seguridad. ... * Botas de Seguridad. ... * Fuentes. """ def Custom_detect(img): model='best' 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_file/', help='source') parser.add_argument('--img-size', type=int, default=100, 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_file/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=3) 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]) #add description gr.Markdown("Este espacio esta hecho para GCC, con el puro echo demostrativo.") gr.Markdown("Para poder usarlo, solo debes de subir una imagen o video, y esperar a que se procese.") gr.Markdown("Si quieres ver el codigo, puedes dar click en el boton de abajo.") inp = gr.Image(type="pil") output = gr.Image(type="pil") examples=[["Examples/Image1.jpg","Image1"],["Examples/Image2.jpg","Image2"]] io=gr.Interface(fn=Custom_detect, inputs=inp, outputs=output, title='Prueba de GuardIA',examples=examples,cache_examples=False) io.launch()