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| from PIL import Image, ImageFilter | |
| import cv2 | |
| import pandas as pd | |
| def get_index(string, substring): | |
| start_index = string.find(substring) # Starting index of "shahzain" | |
| end_index = start_index + len(substring) # Ending index of "shahzain" | |
| return int(start_index), int(end_index) | |
| def get_latest_value_from_csv(search_key): | |
| try: | |
| output_dict = {} | |
| df = pd.read_csv("/content/log.csv") | |
| filtered_df = df[df["OCR Extracted Text"] == search_key] | |
| # Sort the filtered DataFrame by the timestamp column in descending order | |
| sorted_df = filtered_df.sort_values("timestamp", ascending=False) | |
| if not filtered_df.empty: | |
| # Extract the latest row | |
| latest_row = sorted_df.iloc[0] | |
| # Print the values of specific columns that are not null | |
| columns_to_print = ["NAME", "DOB", "GENDER", "COUNTRY", "DOCUMENT NUMBER"] | |
| for column in columns_to_print: | |
| if pd.notnull(latest_row[column]): | |
| output_dict[column] = latest_row[column] | |
| print("Training data from user", output_dict) | |
| else: | |
| print("Annotated Data Not Found") | |
| return True, output_dict | |
| except Exception as e: | |
| print("Exception in e ", str(e)) | |
| return False, output_dict | |
| def image_enhacement(file_path): | |
| image = Image.open(file_path) | |
| upscaled_image = image.resize((image.width*2, image.height*2), resample=Image.Resampling.BILINEAR) | |
| sharpened_image = upscaled_image.filter(ImageFilter.SHARPEN) | |
| sharpened_image.save(file_path) | |
| sharpened_image = cv2.imread(file_path) | |
| smoothed_image = cv2.GaussianBlur(sharpened_image, (5, 5), 0) # Apply smoothing | |
| # Set the desired output level range | |
| output_min = 55 | |
| output_max = 255 | |
| # Calculate the minimum and maximum pixel values in the image | |
| # min_value = np.min(image) | |
| # max_value = np.max(image) | |
| normalized_image = cv2.normalize(smoothed_image, None, output_min, output_max, cv2.NORM_MINMAX) # Normalize the image to the desired output level range | |
| cv2.imwrite(file_path, normalized_image) |