import os import cv2 import time import torch import argparse import gradio as gr import io from PIL import Image from numpy import random from pathlib import Path import torch.backends.cudnn as cudnn from models.experimental import attempt_load import keras_ocr import matplotlib.pyplot as plt from numpy import asarray import pytesseract from datetime import date 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("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt") os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt") pipeline = keras_ocr.pipeline.Pipeline() def detect_Custom(img): model='passport_mrz' # Naming Convention for yolov7 See output file of https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset/data 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='Inference/', help='source') parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.45, 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("Inference/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) 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() 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(cls == 1): x1 = int(xyxy[0].item()) y1 = int(xyxy[1].item()) x2 = int(xyxy[2].item()) y2 = int(xyxy[3].item()) orig_img = im0 crop_img = im0[y1:y2, x1:x2] cv2.imwrite('MRZ_1.png', crop_img) 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) output_text = 'This is not a valid Passport' text = pytesseract.image_to_string(Image.open('MRZ_1.png')) text = text.replace(" ", "") text=text[22:28] today = date.today() s = today.strftime('%Y%m%d')[2:] if(text > s): output_text = 'This is a Valid Passport' #images = [keras_ocr.tools.read(img) for img in [boundedImage]] #prediction_groups = pipeline.recognize(images) #first=prediction_groups[0] #for text,box in first: #output_text += ' '+ text 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]), output_text output = gr.Textbox(label="Validation",elem_id="opbox") Custom_description="
Custom Training Performed on Colab Link

Model trained with test dataset of 'aadhar-card', 'credit-card','prescription' and 'passport'
" Footer = ( "
Model Trained by: Owais Ahmad Data Scientist at Thoucentric Visit Profile
" "
Model Trained Kaggle Kernel Link
" "
Kaggle Profile Link
" "
HuggingFace🤗 Model Deployed Repository Link
" ) examples1=[["Image1.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image2.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image3.jpeg", "Yolo_v7_Custom_trained_By_Owais",],["Image4.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image5.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image6.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["horses.jpeg", "yolov7"],["horses.jpeg", "yolov7-e6"]] Top_Title="
Intelligent Image to Text - IIT
" css = ".output-image, .input-image, .image-preview {height: 300px !important}" gr.Interface(detect_Custom,gr.Image(type="pil"),[gr.Image(type="pil"),output],css=css,title=Top_Title,examples=examples1,description=Custom_description,article=Footer,cache_examples=False).launch()