converting to base64 instead of uint8

DeFogify_Main.py

@@ -2,19 +2,19 @@ import cv2
 import numpy as np
 import gradio as gr
 
-def dark_channel(img, size=15):
+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)
+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):
+def get_trans(img, atom, w = 0.95):
     x = img / atom
     t = 1 - w * dark_channel(x, 15)
     return t
@@ -40,13 +40,16 @@ def dehaze(image):
     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)
+    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
 
-    return (result * 255).astype(np.uint8)  # expected images in the uint8 format (pixel values between 0 and 255)
+    # Ensure the result is in the range [0, 1]
+    result = np.clip(result, 0, 1)
+    return (result * 255).astype(np.uint8)  
 
-PixelDehazer = gr.Interface(fn=dehaze, inputs=gr.Image(type="numpy"), outputs="image") # passed image as numpy array
+# Create Gradio interface
+PixelDehazer = gr.Interface(fn=dehaze, inputs=gr.Image(type="numpy"), outputs="image")
 PixelDehazer.launch()