martinnecp/document_processing

1import gradio as gr2import os3import tempfile4import cv25import numpy as np6 7# function that calculate lower boun level 8# it was defined experimentaly and means when M as a mean walue of gryscale is 180 then lower bound walue is 1309# when mean walue of gry scale pixels is 90 then lower baund value is 7010def calculate_LowerBound(M):11    # Define the slope (m) and intercept (b)12    m = 2 / 313    b = 1014 15    # Calculate B based on M16    B = m * M + b17    return B18 19# Function to process the image by dividing it into blocks and returns 25 blocks of image20def process_image_in_blocks(image, rows=5, cols=5):21    blocks = []22    height, width = image.shape[:2]23 24    # Calculate the size of each block25    block_height = height // rows26    block_width = width // cols27 28    # Create an empty array to store the processed image29    processed_image = np.zeros_like(image)30 31    for i in range(rows):32        for j in range(cols):33            # Extract each block from the image34            y_start = i * block_height35            y_end = (i + 1) * block_height if i != rows - 1 else height36            x_start = j * block_width37            x_end = (j + 1) * block_width if j != cols - 1 else width38 39            block = image[y_start:y_end, x_start:x_end]40            blocks.append(block)41            #cv2.imwrite(f'block.png{i},{j}.png', block) ## prepare to save image to see it42    return blocks43 44def change_image(image,lower_bound = 100):45 46    # Assuming 'image' is your RGB image (shape = (2777, 1903, 3))47    # For example, using a dummy image for demonstration48    # image = np.random.randint(0, 256, (2777, 1903, 3), dtype=np.uint8)49 50    # Define the range of values for the R, G, or B channels51    upper_bound = 25552 53    # Create a condition where any channel (R, G, or B) is in the range [50, 255]54    condition = (image[:, :, 0] >= lower_bound) & (image[:, :, 0] <= upper_bound) | \55               (image[:, :, 1] >= lower_bound) & (image[:, :, 1] <= upper_bound) | \56               (image[:, :, 2] >= lower_bound) & (image[:, :, 2] <= upper_bound)57 58    # Set the RGB value to white where the condition is true59    image[condition] = [255, 255, 255]60 61    # Now, 'image' has white pixels where any of the R, G, or B channels were in the range [50, 255]62    return image63 64 65def process_image(file):66    image = cv2.imread(file.name)  # file is a tempfile._TemporaryFileWrapper67    68    block_images = process_image_in_blocks(image=image) # vrati roylo6ene obraykz na bloky69        70    ch_blocks = [] # will be list of changed blocks 71    for part_image in block_images:72        ll = calculate_LowerBound(np.mean(part_image)) # calculate lower bound od picel which pixels will be removed from image is obtain experimentaly73        changed = change_image(part_image,lower_bound=ll) # remove pixel of lower boud 74        ch_blocks.append(changed) # create new list 75 76    # Assuming 'ch_blocks' is a list of 25 blocks (e.g., 5x5 grid)77    # Concatenate the blocks horizontally in each row, and then concatenate the rows vertically78 79    # List to store the rows80    rows = []81    # Loop to concatenate blocks in each row82    for i in range(0, 25, 5):  # Step size of 5 to group the blocks into rows83        row = np.concatenate(ch_blocks[i:i+5], axis=1)  # Concatenate blocks horizontally (axis=1)84        rows.append(row)85    # Concatenate the rows vertically to form the final image86    cc = np.concatenate(rows, axis=0)  # Concatenate along the vertical axis (axis=0)    87    88    image = cc # test to be image OK89 90    temp_dir = tempfile.mkdtemp()91    file_path = os.path.join(temp_dir, "bw_image.png")92    cv2.imwrite(file_path, image)93    return image, file_path94 95with gr.Blocks() as demo:96    with gr.Tabs():97        with gr.Tab("Home"):98            gr.Markdown("## Welcome to the Home Page")99        with gr.Tab("Settings"):100            gr.Markdown("## Adjust your settings here")101        with gr.Tab("My getting started"):102            gr.Markdown("## Upload an image, adjust brightness, and download the result")103 104            with gr.Row():105                    cv_adjust = gr.Button('cv', variant="primary")106 107            with gr.Row():108                cv_file_input = gr.File(label="Upload an image")109                cv_image_output = gr.Image(type="numpy", width=300, height=300)110                fileoutput = gr.File(label="Dovnlosad processed file")111 112    cv_adjust.click(113        fn = process_image,114        inputs = cv_file_input,115        outputs = [cv_image_output, fileoutput]116    )117 118if __name__ == "__main__":119    demo.launch()