Spaces:
Runtime error
Runtime error
File size: 9,251 Bytes
ed5c13a 68398ed ed5c13a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
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 nest_asyncio
nest_asyncio.apply()
# 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
# FONT_PATH is no longer used since we generate handwriting via Calligraphr
# FONT_PATH = "./STEVEHANDWRITING-REGULAR.TTF"
# ----------------------------
# Pyppeteer: Generate handwriting image via Calligraphr
# ----------------------------
async def generate_handwriting_text_image(text_prompt, screenshot_path):
browser = await launch(headless=True, args=['--no-sandbox', '--disable-setuid-sandbox'])
page = await browser.newPage()
# Navigate to Calligraphr (adjust URL if needed)
await page.goto('https://www.calligraphr.com/en/font/', {'waitUntil': 'networkidle2'})
# Wait for the text input to be available and type the text
await page.waitForSelector('#text-input')
await page.type('#text-input', text_prompt)
# Wait for the page to render the handwriting preview
await asyncio.sleep(2)
# Take a screenshot of the area containing the rendered handwriting text.
# (Adjust the clip values if needed to capture the correct area.)
await page.screenshot({
'path': screenshot_path,
'clip': {'x': 100, 'y': 200, 'width': 600, 'height': 150}
})
await browser.close()
logging.debug(f"Calligraphr screenshot saved at {screenshot_path}")
return screenshot_path
# ----------------------------
# Helper: Detect paper angle within bounding box
# ----------------------------
def detect_paper_angle(image, bounding_box):
x1, y1, x2, y2 = bounding_box
# Crop the region of interest (ROI) based on the bounding box
roi = np.array(image)[y1:y2, x1:x2]
# Convert ROI to grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_RGBA2GRAY)
# Apply edge detection
edges = cv2.Canny(gray, 50, 150)
# Detect lines using Hough Line Transformation
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=50, maxLineGap=10)
if lines is not None:
# Find the longest line (most prominent edge)
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]
# Calculate the angle of the line relative to the horizontal axis
dx = x2_line - x1_line
dy = y2_line - y1_line
angle = degrees(atan2(dy, dx))
return angle # Angle of the paper
else:
return 0 # Default to no rotation if no lines are found
# ----------------------------
# 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}")
# Open the image for processing
pil_image = image.convert("RGBA")
logging.debug("Converted image to RGBA mode.")
# Iterate over detected objects (assumed white papers)
for obj in prediction['predictions']:
# Use white paper dimensions from the prediction
white_paper_width = obj['width']
white_paper_height = obj['height']
# Set padding (adjust percentages as needed)
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}.")
# Calculate padded coordinates
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)
# Detect paper angle
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 the 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.")
# --------------------------------------------
# New: Generate handwriting image via Calligraphr
# --------------------------------------------
handwriting_path = "/tmp/handwriting.png"
try:
# Run the async Pyppeteer function to generate handwriting
handwriting_path = asyncio.run(generate_handwriting_text_image(text, handwriting_path))
except Exception as e:
logging.error(f"Error generating handwriting image: {e}")
continue # Optionally, you could fall back to another method here
# Open the generated handwriting image
handwriting_img = Image.open(handwriting_path).convert("RGBA")
# Resize handwriting image to fit the white paper box
handwriting_img = handwriting_img.resize((box_width, box_height), Image.ANTIALIAS)
# Rotate the handwriting image to align with the detected paper angle
rotated_handwriting = handwriting_img.rotate(-angle, resample=Image.BICUBIC, expand=True)
# Composite the rotated handwriting image onto a transparent layer,
# then overlay it on the original image
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.")
# Save and return output image path
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"), # Preview processed image
gr.File(label="Download Processed Image"), # Download the image
gr.Textbox(label="Status") # Status message
],
title="Roboflow Detection with Handwriting Overlay",
description="Upload an image, 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"
)
# Launch the Gradio app
if __name__ == "__main__":
logging.debug("Launching Gradio interface.")
interface.launch(share=True)
|