Main file added

DeFogify_Main.py

@@ -0,0 +1,52 @@
+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 = cv2.max(trans_guided, 0.25)
+
+    result = np.empty_like(img)
+    for i in range(3):
+        result[:, :, i] = (img[:, :, i] - atom) / trans_guided + atom
+
+    return (result * 255).astype(np.uint8)  # expected images in the uint8 format (pixel values between 0 and 255)
+
+PixelDehazer = gr.Interface(fn=dehaze, inputs=gr.Image(type="numpy"), outputs="image") # passed image as numpy array
+PixelDehazer.launch()