For dehazing all the images in the current directory and save them in the specified directory.

dehaze_multi.py

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+import os
+import cv2
+import math
+import numpy as np
+import sys
+
+def apply_mask(matrix, mask, fill_value):
+    masked = np.ma.array(matrix, mask=mask, fill_value=fill_value)
+    print('MASKED=',masked)
+    return masked.filled()
+
+def apply_threshold(matrix, low_value, high_value):
+    low_mask = matrix < low_value
+    matrix = apply_mask(matrix, low_mask, low_value)
+    print('Low MASK->',low_mask,'\nMatrix->',matrix)
+
+    high_mask = matrix > high_value
+    matrix = apply_mask(matrix, high_mask, high_value)
+    print('high MASK->',high_mask,'\nMatrix->',matrix)
+
+    return matrix
+
+def simplest_cb(img, percent):
+    assert img.shape[2] == 3
+    assert percent > 0 and percent < 100
+
+    half_percent = percent / 200.0
+    print('HALF PERCENT->',half_percent)
+
+    channels = cv2.split(img)
+    print('Channels->\n',channels)
+    print('Shape->',channels[0].shape)
+    print('Shape of channels->',len(channels[2]))
+
+    out_channels = []
+    for channel in channels:
+        assert len(channel.shape) == 2
+        # find the low and high precentile values (based on the input percentile)
+        height, width = channel.shape
+        vec_size = width * height
+        flat = channel.reshape(vec_size)
+        print('vec=',vec_size,'\nFlat=',flat)
+        print(flat[1009])
+        assert len(flat.shape) == 1
+
+        flat = np.sort(flat)
+
+        n_cols = flat.shape[0]
+
+        low_val  = flat[math.floor(n_cols * half_percent)]
+        high_val = flat[math.ceil( n_cols * (1.0 - half_percent))]
+        print(math.floor(n_cols*half_percent))
+        print(math.ceil(n_cols*half_percent))
+        print(math.floor(n_cols*(1-half_percent)))
+        print(math.ceil(n_cols*(1-half_percent)))
+
+        print ("sorted flat: ", flat)
+        print ("n_cols: ", n_cols)
+        print ("Lowval: ", low_val)
+        print ("Highval: ", high_val)
+
+        # saturate below the low percentile and above the high percentile
+        thresholded = apply_threshold(channel, low_val, high_val)
+        # scale the channel
+        normalized = cv2.normalize(thresholded, thresholded.copy(), 0, 255, cv2.NORM_MINMAX)
+        out_channels.append(normalized)
+
+    return cv2.merge(out_channels)
+
+#img = cv2.imread(sys.argv[1])
+directory = 'G:\\OPEN_CV_\\X'
+files = os.listdir(directory)
+filepaths = [os.path.join(directory,i) for i in files]
+print(filepaths)
+
+for i in filepaths:
+
+	img = cv2.imread(i)
+	out = simplest_cb(img, 1)
+	'''cv2.imshow("Before", img)
+	cv2.imshow("After", out)
+	cv2.waitKey(0)'''
+	cv2.imwrite(directory+'\\Dehazed\\'+i.split('\\')[3].split('.')[0]+'_dehazed.jpg', out)