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import gradio as gr
import cv2
import numpy as np
from skimage import io
def edge_detection(image, threshold1, threshold2):
"""邊緣檢測"""
###先將圖片轉成灰度
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
###再用Canny算法檢測邊緣
edges = cv2.Canny(gray, threshold1, threshold2)
return edges
def line_detection(image, threshold, min_line_length, max_line_gap):
"""直線檢測"""
##前面先做邊緣檢測的部分
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
##用Hough找出直線
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold, minLineLength=min_line_length, maxLineGap=max_line_gap)
line_image = np.zeros_like(image)
##之後再化回原圖
if lines is not None:
for line in lines:
x1, y1, x2, y2 = line[0]
cv2.line(line_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
combined = cv2.addWeighted(image, 0.8, line_image, 1, 0)
return combined
def image_inpainting(image, mask_radius):
"""圖像修復"""
mask = np.zeros(image.shape[:2], dtype=np.uint8)
cv2.circle(mask, (image.shape[1]//2, image.shape[0]//2), mask_radius, 255, -1)
inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
return inpainted
# Example images for easy testing
example_images = [
("Edge Image 1", "https://www.sanrio.com.tw/wp-content/uploads/2018/09/25.%E6%98%8E%E6%98%9F%E4%BB%8B%E7%B4%B9KU-02.png"),
("Edge Image 2", "https://web.hocom.tw/Uploads/Product/21380_470807_ji9n9psb.jpg"),
("Line Image 1", "https://p3-sdbk2-media.byteimg.com/tos-cn-i-xv4ileqgde/5cc733d480b64417a7b9a5adc53e4f72~tplv-xv4ileqgde-resize-w:750.image"),
("Line Image 2", "https://imgs.699pic.com/images/321/926/661.jpg!detail.v1"),
("Inpaint Image 1","https://img95.699pic.com/xsj/0v/ig/gj.jpg!/fh/300"),
("Inpaint Image 2","https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS0bxiS49IMEuZs7JjZx54JPw8x6s-YaONtPQ&s")
]
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## Computer Vision Web Application")
with gr.Tab("Edge Detection"):
with gr.Row():
with gr.Column():
img_input = gr.Image(type="numpy", label="Input Image")
threshold1 = gr.Slider(0, 255, 50, step=1, label="Threshold 1")
threshold2 = gr.Slider(0, 255, 150, step=1, label="Threshold 2")
with gr.Column():
edge_output = gr.Image(label="Edge Detection Result")
edge_button = gr.Button("Run Edge Detection")
edge_button.click(edge_detection, inputs=[img_input, threshold1, threshold2], outputs=edge_output)
with gr.Tab("Line Detection"):
with gr.Row():
with gr.Column():
img_input_line = gr.Image(type="numpy", label="Input Image")
threshold = gr.Slider(1, 100, 50, step=1, label="Threshold")
min_line_length = gr.Slider(10, 200, 50, step=1, label="Min Line Length")
max_line_gap = gr.Slider(1, 50, 10, step=1, label="Max Line Gap")
with gr.Column():
line_output = gr.Image(label="Line Detection Result")
line_button = gr.Button("Run Line Detection")
line_button.click(line_detection, inputs=[img_input_line, threshold, min_line_length, max_line_gap], outputs=line_output)
with gr.Tab("Image Inpainting"):
with gr.Row():
with gr.Column():
img_input_inpaint = gr.Image(type="numpy", label="Input Image")
mask_radius = gr.Slider(10, 100, 30, step=1, label="Mask Radius")
with gr.Column():
inpaint_output = gr.Image(label="Inpainting Result")
inpaint_button = gr.Button("Run Inpainting")
inpaint_button.click(image_inpainting, inputs=[img_input_inpaint, mask_radius], outputs=inpaint_output)
gr.Markdown("### Example Images")
for label, url in example_images:
gr.Markdown(f"[{label}]({url})")
# Launch Gradio app
demo.launch()
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