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| import gradio as gr | |
| import logging | |
| from roboflow import Roboflow | |
| from PIL import Image, ImageDraw | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from math import atan2, degrees | |
| import asyncio | |
| from pyppeteer import launch | |
| import multiprocessing | |
| # Configure logging | |
| logging.basicConfig( | |
| level=logging.DEBUG, | |
| format='%(asctime)s - %(levelname)s - %(message)s', | |
| handlers=[ | |
| logging.FileHandler("debug.log"), | |
| logging.StreamHandler() | |
| ] | |
| ) | |
| # Roboflow and model configuration | |
| ROBOFLOW_API_KEY = "KUP9w62eUcD5PrrRMJsV" # Replace with your API key | |
| PROJECT_NAME = "model_verification_project" | |
| VERSION_NUMBER = 2 | |
| # ---------------------------- | |
| # New: Run Pyppeteer code in a separate process | |
| # ---------------------------- | |
| def generate_handwriting_image_process(text_prompt, screenshot_path, return_dict): | |
| """ | |
| This function runs in a separate process so that the Pyppeteer code | |
| runs in the main thread of that process. | |
| """ | |
| import asyncio | |
| from pyppeteer import launch | |
| async def _generate(): | |
| browser = await launch(headless=True, args=['--no-sandbox', '--disable-setuid-sandbox']) | |
| page = await browser.newPage() | |
| await page.goto('https://www.calligraphr.com/en/font/', {'waitUntil': 'networkidle2'}) | |
| await page.waitForSelector('#text-input') | |
| await page.type('#text-input', text_prompt) | |
| await asyncio.sleep(2) # Wait for the handwriting preview to render | |
| # Adjust these clip dimensions as needed for the correct area | |
| await page.screenshot({ | |
| 'path': screenshot_path, | |
| 'clip': {'x': 100, 'y': 200, 'width': 600, 'height': 150} | |
| }) | |
| await browser.close() | |
| return screenshot_path | |
| # Create a new event loop for this process | |
| loop = asyncio.new_event_loop() | |
| asyncio.set_event_loop(loop) | |
| result = loop.run_until_complete(_generate()) | |
| return_dict['result'] = result | |
| def get_handwriting_image(text_prompt, screenshot_path="/tmp/handwriting.png"): | |
| manager = multiprocessing.Manager() | |
| return_dict = manager.dict() | |
| process = multiprocessing.Process(target=generate_handwriting_image_process, args=(text_prompt, screenshot_path, return_dict)) | |
| process.start() | |
| process.join() | |
| return return_dict.get('result', None) | |
| # ---------------------------- | |
| # Helper: Detect paper angle within bounding box | |
| # ---------------------------- | |
| def detect_paper_angle(image, bounding_box): | |
| x1, y1, x2, y2 = bounding_box | |
| roi = np.array(image)[y1:y2, x1:x2] | |
| gray = cv2.cvtColor(roi, cv2.COLOR_RGBA2GRAY) | |
| edges = cv2.Canny(gray, 50, 150) | |
| lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=50, maxLineGap=10) | |
| if lines is not None: | |
| longest_line = max(lines, key=lambda line: np.linalg.norm((line[0][2] - line[0][0], line[0][3] - line[0][1]))) | |
| x1_line, y1_line, x2_line, y2_line = longest_line[0] | |
| dx = x2_line - x1_line | |
| dy = y2_line - y1_line | |
| angle = degrees(atan2(dy, dx)) | |
| return angle | |
| else: | |
| return 0 | |
| # ---------------------------- | |
| # Main processing function | |
| # ---------------------------- | |
| def process_image(image, text): | |
| try: | |
| # Initialize Roboflow | |
| rf = Roboflow(api_key=ROBOFLOW_API_KEY) | |
| logging.debug("Initialized Roboflow API.") | |
| project = rf.workspace().project(PROJECT_NAME) | |
| logging.debug("Accessed project in Roboflow.") | |
| model = project.version(VERSION_NUMBER).model | |
| logging.debug("Loaded model from Roboflow.") | |
| # Save input image temporarily | |
| input_image_path = "/tmp/input_image.jpg" | |
| image.save(input_image_path) | |
| logging.debug(f"Input image saved to {input_image_path}.") | |
| # Perform inference | |
| logging.debug("Performing inference on the image...") | |
| prediction = model.predict(input_image_path, confidence=70, overlap=50).json() | |
| logging.debug(f"Inference result: {prediction}") | |
| pil_image = image.convert("RGBA") | |
| logging.debug("Converted image to RGBA mode.") | |
| # Process each detected object (assumed to be white paper) | |
| for obj in prediction['predictions']: | |
| white_paper_width = obj['width'] | |
| white_paper_height = obj['height'] | |
| padding_x = int(white_paper_width * 0.1) | |
| padding_y = int(white_paper_height * 0.1) | |
| box_width = white_paper_width - 2 * padding_x | |
| box_height = white_paper_height - 2 * padding_y | |
| logging.debug(f"Padded white paper dimensions: width={box_width}, height={box_height}.") | |
| x1_padded = int(obj['x'] - white_paper_width / 2 + padding_x) | |
| y1_padded = int(obj['y'] - white_paper_height / 2 + padding_y) | |
| x2_padded = int(obj['x'] + white_paper_width / 2 - padding_x) | |
| y2_padded = int(obj['y'] + white_paper_height / 2 - padding_y) | |
| angle = detect_paper_angle(np.array(image), (x1_padded, y1_padded, x2_padded, y2_padded)) | |
| logging.debug(f"Detected paper angle: {angle} degrees.") | |
| # For debugging: draw bounding box (optional) | |
| debug_layer = pil_image.copy() | |
| debug_draw = ImageDraw.Draw(debug_layer) | |
| debug_draw.rectangle([(x1_padded, y1_padded), (x2_padded, y2_padded)], outline="red", width=3) | |
| debug_layer.save("/tmp/debug_bounding_box.png") | |
| logging.debug("Saved bounding box debug image to /tmp/debug_bounding_box.png.") | |
| # Generate handwriting image using the separate process | |
| handwriting_path = get_handwriting_image(text, "/tmp/handwriting.png") | |
| if not handwriting_path: | |
| logging.error("Handwriting image generation failed.") | |
| continue | |
| handwriting_img = Image.open(handwriting_path).convert("RGBA") | |
| handwriting_img = handwriting_img.resize((box_width, box_height), Image.ANTIALIAS) | |
| rotated_handwriting = handwriting_img.rotate(-angle, resample=Image.BICUBIC, expand=True) | |
| text_layer = Image.new("RGBA", pil_image.size, (255, 255, 255, 0)) | |
| paste_x = int(obj['x'] - rotated_handwriting.size[0] / 2) | |
| paste_y = int(obj['y'] - rotated_handwriting.size[1] / 2) | |
| text_layer.paste(rotated_handwriting, (paste_x, paste_y), rotated_handwriting) | |
| pil_image = Image.alpha_composite(pil_image, text_layer) | |
| logging.debug("Handwriting layer composited onto the original image.") | |
| output_image_path = "/tmp/output_image.png" | |
| pil_image.convert("RGB").save(output_image_path) | |
| logging.debug(f"Output image saved to {output_image_path}.") | |
| return output_image_path | |
| except Exception as e: | |
| logging.error(f"Error during image processing: {e}") | |
| return None | |
| # ---------------------------- | |
| # Gradio interface function | |
| # ---------------------------- | |
| def gradio_inference(image, text): | |
| logging.debug("Starting Gradio inference.") | |
| result_path = process_image(image, text) | |
| if result_path: | |
| logging.debug("Gradio inference successful.") | |
| return result_path, result_path, "Processing complete! Download the image below." | |
| logging.error("Gradio inference failed.") | |
| return None, None, "An error occurred while processing the image. Please check the logs." | |
| # ---------------------------- | |
| # Gradio interface definition | |
| # ---------------------------- | |
| interface = gr.Interface( | |
| fn=gradio_inference, | |
| inputs=[ | |
| gr.Image(type="pil", label="Upload an Image"), | |
| gr.Textbox(label="Enter Text to Overlay") | |
| ], | |
| outputs=[ | |
| gr.Image(label="Processed Image Preview"), | |
| gr.File(label="Download Processed Image"), | |
| gr.Textbox(label="Status") | |
| ], | |
| title="Roboflow Detection with Handwriting Overlay", | |
| description="Upload an image and enter text to overlay. The Roboflow model detects the white paper area, and a handwriting image is generated via Calligraphr using Pyppeteer. The output image is composited accordingly.", | |
| allow_flagging="never" | |
| ) | |
| if __name__ == "__main__": | |
| logging.debug("Launching Gradio interface.") | |
| interface.launch(share=True) | |