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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image, ImageDraw | |
| # import imutils | |
| # import easyocr | |
| # import os | |
| # import pathlib | |
| # import platform | |
| # from xyxy_converter import yolov5_to_image_coordinates | |
| # import shutil | |
| from extractor import get_card_xy, get_digit | |
| # system_platform = platform.system() | |
| # if system_platform == 'Windows': pathlib.PosixPath = pathlib.WindowsPath | |
| # CUR_DIR = os.getcwd() | |
| # YOLO_PATH = f"{CUR_DIR}/yolov5" | |
| # MODEL_PATH = "runs/train/exp/weights/best.pt" | |
| def main(): | |
| st.title("Card number detector") | |
| # Use st.camera to capture images from the user's camera | |
| img_file_buffer = st.camera_input(label='Please, take a photo of a card', key='card') | |
| # try: | |
| # image = Image.open(img_file_buffer) | |
| # except: | |
| # st.write('No shot detected') | |
| # Check if an image is captured | |
| if img_file_buffer is not None: | |
| # Convert the image to a NumPy array | |
| image = Image.open(img_file_buffer) | |
| image_np = np.array(image) | |
| resized_image = cv2.resize(image_np, (640, 640)) | |
| resized_image = resized_image.astype(np.uint8) | |
| resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB) | |
| cv2.imwrite('card_image.jpg', resized_image) | |
| # original_img = cv2.imread('card_image.jpg') | |
| gray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY) | |
| x1, y1, x2, y2, card_confidence = get_card_xy( | |
| model_path='credit_card_number_detector.tflite', | |
| image_path='card_image.jpg' | |
| ) | |
| if card_confidence == 0: | |
| display_text = "A card is not detected in the image!!!" | |
| st.image('card_image.jpg', caption=f"{display_text}", use_column_width=True) | |
| else: | |
| # cropped_image = gray[y1:y2, x1:x2] | |
| # # cropped_image = resized_image[y1:y2, x1:x2] | |
| # cropped_image = cv2.resize(cropped_image, (128, 128)) | |
| # cv2.imwrite('card_number_image.jpg', cropped_image) | |
| # extracted_digit = get_digit( | |
| # model_path="card_number_extractor.tflite", | |
| # image_path='card_number_image.jpg', | |
| # threshold=0.4 | |
| # ) | |
| # display_text = f'Here is the zoomed card number: {extracted_digit}' | |
| # st.image('card_number_image.jpg', caption=f"{display_text}", use_column_width=True) | |
| image = Image.open('card_image.jpg') | |
| image_resized = image.resize((640, 640)) | |
| draw = ImageDraw.Draw(image_resized) | |
| draw.rectangle([x1, y1, x2, y2], outline="red", width=2) | |
| class_name = 'card' | |
| text = f"Class: {class_name}, Confidence: {card_confidence:.2f}" | |
| draw.text((x1, y1), text, fill="red") | |
| # Saving Images | |
| image_resized.save('card_highlighted_image.jpg') | |
| display_text = f'Here is the card number on the image with {card_confidence:.4f} confidence.' | |
| st.write(f"{display_text}") | |
| # st.image('card_highlighted_image.jpg', caption=f"{display_text}", use_column_width=True) | |
| st.image('card_highlighted_image.jpg', use_column_width=True) | |
| st.session_state.pop("card") | |
| if __name__ == "__main__": | |
| main() |