Spaces:
Sleeping
Sleeping
File size: 2,795 Bytes
46ff125 |
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 |
import cv2
import numpy as np
import gradio as gr
from PIL import Image
def resize_to_512(img: Image.Image) -> Image.Image:
if img.size != (512, 512):
return img.resize((512, 512))
return img
def gaussian_blur(img: Image.Image, kernel_size: int):
img = resize_to_512(img)
img_cv = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
blurred = cv2.GaussianBlur(img_cv, (kernel_size | 1, kernel_size | 1), 0)
return cv2.cvtColor(blurred, cv2.COLOR_BGR2RGB)
def lens_blur(img: Image.Image, max_blur_radius: int):
img = resize_to_512(img)
original = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
original_rgb = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
# Create synthetic depth map
depth_norm = np.zeros((original.shape[0], original.shape[1]), dtype=np.float32)
cv2.circle(depth_norm, (original.shape[1] // 2, original.shape[0] // 2), 100, 1, -1)
depth_norm = cv2.GaussianBlur(depth_norm, (21, 21), 0)
blurred_image = np.zeros_like(original_rgb)
for i in range(original.shape[0]):
for j in range(original.shape[1]):
blur_radius = int(depth_norm[i, j] * max_blur_radius)
if blur_radius % 2 == 0:
blur_radius += 1
x_min = max(j - blur_radius, 0)
x_max = min(j + blur_radius, original.shape[1])
y_min = max(i - blur_radius, 0)
y_max = min(i + blur_radius, original.shape[0])
roi = original_rgb[y_min:y_max, x_min:x_max]
if blur_radius > 1:
blurred_roi = cv2.GaussianBlur(roi, (blur_radius, blur_radius), 0)
try:
blurred_image[i, j] = blurred_roi[
blur_radius // 2, blur_radius // 2
]
except:
blurred_image[i, j] = original_rgb[i, j]
else:
blurred_image[i, j] = original_rgb[i, j]
return blurred_image
with gr.Blocks() as demo:
gr.Markdown("## Gaussian and Lens Blur App")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload an Image")
with gr.Row():
kernel_slider = gr.Slider(1, 49, value=11, step=2, label="Gaussian Kernel Size")
max_blur_slider = gr.Slider(
1, 50, value=15, step=1, label="Max Lens Blur Radius"
)
with gr.Row():
gaussian_output = gr.Image(label="Gaussian Blurred Image")
lens_output = gr.Image(label="Depth-Based Lens Blurred Image")
with gr.Row():
blur_btn = gr.Button("Apply Blur")
blur_btn.click(
fn=gaussian_blur, inputs=[image_input, kernel_slider], outputs=gaussian_output
)
blur_btn.click(
fn=lens_blur, inputs=[image_input, max_blur_slider], outputs=lens_output
)
demo.launch()
|