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import gradio as gr
import cv2
import numpy as np
from pathlib import Path
# Import the existing homography and warping function from your existing script
from billboard import apply_homography_and_warp
# Function to select points on the image
def get_point_interface(original_frame, points, evt: gr.SelectData):
x, y = evt.index
if points is None:
points = []
points = points.copy() # Make a copy to avoid modifying in-place
points.append((x, y))
# Draw the points and lines on the image
roi_image = original_frame.copy()
for pt in points:
cv2.circle(roi_image, pt, 5, (255, 0, 0), -1)
if len(points) > 1:
for i in range(len(points) - 1):
cv2.line(roi_image, points[i], points[i + 1], (255, 0, 0), 2)
return roi_image, points
# Function to process and apply homography (this will call your existing function)
def process_images(source_image, dest_image, roi_points):
# Ensure that exactly four points are selected
if len(roi_points) != 4:
return "Error: Please select exactly four points."
# Convert images to correct format (if not already in uint8)
img_src = np.array(source_image)
img_dst = np.array(dest_image)
# Apply homography and get the warped image
result_img = apply_homography_and_warp(img_src, img_dst, np.array(roi_points))
# Return the processed image
return result_img
# Gradio interface setup
with gr.Blocks(title="Homography Warping App", theme=gr.themes.Soft()) as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Homography Warping App
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://opencv.org/university/' target='_blank'>OpenCV Courses</a> | <a href='https://github.com/OpenCV-University' target='_blank'>Github</a>
</h3>
"""
)
gr.Markdown(
"Upload two images (source and destination), select the four points for ROI on the destination image, and click 'Process Images' to warp the source image onto the destination."
)
with gr.Row():
with gr.Column(scale=1):
with gr.Column():
source_image_input = gr.Image(label="Upload Source Image")
dest_image_input = gr.Image(label="Upload Destination Image")
with gr.Column(scale=1):
dest_image_copy = gr.Image(label="Select ROI points on destination image")
original_frame_state = gr.State(None)
selected_points = gr.State([])
clear_points_button = gr.Button("Clear Points")
process_button = gr.Button("Process Images")
with gr.Column(scale=1):
output_image = gr.Image(label="Wrapped Image")
examples = [
["./images/Apollo-8-Launch.png", "./images/times_square.jpg"],
]
with gr.Row():
gr.Examples(
examples=examples,
inputs=[source_image_input, dest_image_input],
label="Load Example Images",
)
dest_image_input.upload(
lambda img:img, inputs=dest_image_input, outputs=dest_image_copy # Simply return the uploaded image
)
# Adding the ROI point selection functionality
dest_image_copy.select(get_point_interface, inputs=[dest_image_copy, selected_points], outputs=[dest_image_copy, selected_points])
# Clear the selected points
clear_points_button.click(
fn=lambda original_frame: (original_frame, []),
inputs=dest_image_input,
outputs=[dest_image_input, selected_points],
)
# Callback for processing the images
process_button.click(
process_images, inputs=[source_image_input, dest_image_input, selected_points], outputs=output_image
)
# Launch the Gradio app
demo.launch()
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