# import os # import sys # import argparse # import time # from pathlib import Path # import pandas as pd import gradio as gr # import cv2 from PIL import Image # import torch # import torch.backends.cudnn as cudnn # from numpy import random import numpy as np # BASE_DIR = "/home/user/app" # os.chdir(BASE_DIR) # os.makedirs(f"{BASE_DIR}/input",exist_ok=True) # os.system(f"git clone https://github.com/WongKinYiu/yolov7.git {BASE_DIR}/yolov7") # sys.path.append(f'{BASE_DIR}/yolov7') # def detect(opt, save_img=False): # from models.experimental import attempt_load # 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 # bbox = {} # 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 # save_img = not opt.nosave and not source.endswith('.txt') # save inference images # webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( # ('rtsp://', 'rtmp://', 'http://', 'https://')) # # Directories # save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run # (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # # Initialize # set_logging() # device = select_device(opt.device) # half = device.type != 'cpu' # half precision only supported on CUDA # # Load model # model = attempt_load(weights, map_location=device) # load FP32 model # stride = int(model.stride.max()) # model stride # imgsz = check_img_size(imgsz, s=stride) # check img_size # if trace: # model = TracedModel(model, device, opt.img_size) # 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']).to(device).eval() # # Set Dataloader # vid_path, vid_writer = None, None # if webcam: # view_img = check_imshow() # cudnn.benchmark = True # set True to speed up constant image size inference # dataset = LoadStreams(source, img_size=imgsz, stride=stride) # else: # dataset = LoadImages(source, img_size=imgsz, stride=stride) # # Get names and colors # names = model.module.names if hasattr(model, 'module') else model.names # colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] # # Run inference # if device.type != 'cpu': # model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once # old_img_w = old_img_h = imgsz # old_img_b = 1 # 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() # uint8 to fp16/32 # img /= 255.0 # 0 - 255 to 0.0 - 1.0 # if img.ndimension() == 3: # img = img.unsqueeze(0) # # Warmup # 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]): # old_img_b = img.shape[0] # old_img_h = img.shape[2] # old_img_w = img.shape[3] # for i in range(3): # model(img, augment=opt.augment)[0] # # Inference # t1 = time_synchronized() # with torch.no_grad(): # Calculating gradients would cause a GPU memory leak # pred = model(img, augment=opt.augment)[0] # t2 = time_synchronized() # # Apply NMS # pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) # t3 = 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, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count # else: # p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) # p = Path(p) # to Path # save_path = str(save_dir / p.name) # img.jpg # txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt # gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh # if len(det): # # Rescale boxes from img_size to im0 size # det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # # print(f"BOXES ---->>>> {det[:, :4]}") # bbox[f"{txt_path.split('/')[4]}"]=(det[:, :4]).numpy() # # Print results # for c in det[:, -1].unique(): # n = (det[:, -1] == c).sum() # detections per class # s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # # Write results # for *xyxy, conf, cls in reversed(det): # if save_txt: # Write to file # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh # line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format # with open(txt_path + '.txt', 'a') as f: # f.write(('%g ' * len(line)).rstrip() % line + '\n') # if save_img or view_img: # Add bbox to image # label = f'{names[int(cls)]} {conf:.2f}' # plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) # # Print time (inference + NMS) # print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') # # Stream results # # if view_img: # # cv2.imshow(str(p), im0) # # cv2.waitKey(1) # 1 millisecond # # Save results (image with detections) # if save_img: # if dataset.mode == 'image': # # Image.fromarray(im0).show() # cv2.imwrite(save_path, im0) # print(f" The image with the result is saved in: {save_path}") # # else: # 'video' or 'stream' # # if vid_path != save_path: # new video # # vid_path = save_path # # if isinstance(vid_writer, cv2.VideoWriter): # # vid_writer.release() # release previous video writer # # if vid_cap: # video # # 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: # stream # # 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"Results saved to {save_dir}{s}") # print(f'Done. ({time.time() - t0:.3f}s)') # return bbox,save_path # class options: # def __init__(self, weights, source, img_size=640, conf_thres=0.1, iou_thres=0.45, device='', # view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, # agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', # exist_ok=False, no_trace=False): # self.weights=weights # self.source=source # self.img_size=img_size # self.conf_thres=conf_thres # self.iou_thres=iou_thres # self.device=device # self.view_img=view_img # self.save_txt=save_txt # self.save_conf=save_conf # self.nosave=nosave # self.classes=classes # self.agnostic_nms=agnostic_nms # self.augment=augment # self.update=update # self.project=project # self.name=name # self.exist_ok=exist_ok # self.no_trace=no_trace def get_output(input_image): # image.save(f"{BASE_DIR}/input/image.jpg") # source = f"{BASE_DIR}/input" # opt = options(weights='logo_detection.pt',source=source) # bbox = None # with torch.no_grad(): # bbox,output_path = detect(opt) # if os.path.exists(output_path): # return np.array(Image.open(output_path)) # else: return input_image demo = gr.Interface(fn=get_output, inputs="image", outputs="image") demo.launch(debug=True)