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import cv2
import numpy as np
import gradio as gr
def dark_channel(img, size=15):
r, g, b = cv2.split(img)
min_img = cv2.min(r, cv2.min(g, b))
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))
dc_img = cv2.erode(min_img, kernel)
return dc_img
def get_atmo(img, percent=0.001):
mean_perpix = np.mean(img, axis=2).reshape(-1)
mean_topper = mean_perpix[:int(img.shape[0] * img.shape[1] * percent)]
return np.mean(mean_topper)
def get_trans(img, atom, w=0.95):
x = img / atom
t = 1 - w * dark_channel(x, 15)
return t
def guided_filter(p, i, r, e):
mean_I = cv2.boxFilter(i, cv2.CV_64F, (r, r))
mean_p = cv2.boxFilter(p, cv2.CV_64F, (r, r))
corr_I = cv2.boxFilter(i * i, cv2.CV_64F, (r, r))
corr_Ip = cv2.boxFilter(i * p, cv2.CV_64F, (r, r))
var_I = corr_I - mean_I * mean_I
cov_Ip = corr_Ip - mean_I * mean_p
a = cov_Ip / (var_I + e)
b = mean_p - a * mean_I
mean_a = cv2.boxFilter(a, cv2.CV_64F, (r, r))
mean_b = cv2.boxFilter(b, cv2.CV_64F, (r, r))
q = mean_a * i + mean_b
return q
def dehaze(image):
img = image.astype('float64') / 255
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY).astype('float64') / 255
atom = get_atmo(img)
trans = get_trans(img, atom)
trans_guided = guided_filter(trans, img_gray, 20, 0.0001)
trans_guided = np.maximum(trans_guided, 0.25) # Ensure trans_guided is not below 0.25
result = np.empty_like(img)
for i in range(3):
result[:, :, i] = (img[:, :, i] - atom) / trans_guided + atom
# Ensure the result is in the range [0, 1]
result = np.clip(result, 0, 1)
return (result * 255).astype(np.uint8)
# Save example images for testing
example_images = [
"Sample Images for Testing/ai-generated-9025430_1280.jpg",
"Sample Images for Testing/meadow-5648849_1280.jpg",
"Sample Images for Testing/mountains-7662717_1280.jpg",
"Sample Images for Testing/mountains-8292685_1280.jpg",
"Sample Images for Testing/nature-6722031_1280.jpg"
]
example_paths = []
for i, img_path in enumerate(example_images):
img = cv2.imread(img_path)
save_path = f"example_image_{i+1}.png"
cv2.imwrite(save_path, img)
example_paths.append([save_path])
# Create Gradio interface
PixelDehazer = gr.Interface(
fn=dehaze,
inputs=gr.Image(type="numpy"),
outputs="image",
examples=example_paths,
cache_examples=False
)
PixelDehazer.launch() |