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# Required Libraries
import cv2
import numpy as np
import gradio as gr
def annotate_image(image, grind_degree, detail_degree, strength):
"""Annotates the image with parameters in the lower-left corner."""
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
color = (0, 0, 255)
thickness = 1
line_type = cv2.LINE_AA
# Text positions
y_offset = 20
x_offset = 10
y_base = image.shape[0] - 10
# Define each line of the annotation
lines = [
f"Grind Degree: {grind_degree}",
f"Detail Degree: {detail_degree}",
f"Strength: {strength}",
]
# Draw the text lines on the image
for i, line in enumerate(lines):
y_position = y_base - (i * y_offset)
cv2.putText(
image,
line,
(x_offset, y_position),
font,
font_scale,
color,
thickness,
line_type,
)
return image
def grindSkin(src, grindDegree: int = 3, detailDegree: int = 1, strength: int = 9):
"""
Dest =(Src * (100 - Opacity) + (Src + 2 * GaussBlur(EPFFilter(Src) - Src)) * Opacity) / 100
人像磨皮方案
Args:
src: 原图
grindDegree: 磨皮程度调节参数
detailDegree: 细节程度调节参数
strength: 融合程度，作为磨皮强度（0 - 10）
Returns:
磨皮后的图像
"""
if strength <= 0:
return src
dst = src.copy()
opacity = min(10.0, strength) / 10.0
dx = grindDegree * 5
fc = grindDegree * 12.5
temp1 = cv2.bilateralFilter(src[:, :, :3], dx, fc, fc)
temp2 = cv2.subtract(temp1, src[:, :, :3])
temp3 = cv2.GaussianBlur(temp2, (2 * detailDegree - 1, 2 * detailDegree - 1), 0)
temp4 = cv2.add(cv2.add(temp3, temp3), src[:, :, :3])
dst[:, :, :3] = cv2.addWeighted(temp4, opacity, src[:, :, :3], 1 - opacity, 0.0)
return dst
def process_image(input_img, grind_degree, detail_degree, strength):
# Reading the image using OpenCV
img = cv2.cvtColor(input_img, cv2.COLOR_RGB2BGR)
# Processing the image
output_img = grindSkin(img, grind_degree, detail_degree, strength)
# Annotating the processed image with parameters
output_img_annotated = annotate_image(
output_img.copy(), grind_degree, detail_degree, strength
)
# Horizontal stacking of input and processed images
combined_img = cv2.hconcat([img, output_img_annotated])
# Convert the combined image back to RGB for display
combined_img_rgb = cv2.cvtColor(combined_img, cv2.COLOR_BGR2RGB)
return combined_img_rgb
with gr.Blocks(title="Skin Grinding") as iface:
gr.Markdown("## Skin Grinding Application")
with gr.Row():
image_input = gr.Image(type="numpy", label="Input Image")
image_output = gr.Image(label="Output Image")
grind_degree_slider = gr.Slider(
minimum=1, maximum=10, value=3, step=1, label="Grind Degree"
)
detail_degree_slider = gr.Slider(
minimum=1, maximum=10, value=1, step=1, label="Detail Degree"
)
strength_slider = gr.Slider(
minimum=0, maximum=10, value=9, step=1, label="Strength"
)
gr.Button("Process Image").click(
fn=process_image,
inputs=[
image_input,
grind_degree_slider,
detail_degree_slider,
strength_slider,
],
outputs=image_output,
)
if __name__ == "__main__":
iface.launch()
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