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app.py

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+import numpy as np
+import cv2
+import os
+
+#input_dir = "input_images/"
+# input_dir = ""
+# input_image_list = os.listdir(input_dir)
+
+ #output_dir = "output_images/"
+# output_dir = ""
+
+def get_mean_and_std(x):
+	x_mean, x_std = cv2.meanStdDev(x)
+	x_mean = np.hstack(np.around(x_mean,2))
+	x_std = np.hstack(np.around(x_std,2))
+	return x_mean, x_std
+
+template_img = cv2.imread('cherry-blossom.jpg')
+template_img = cv2.cvtColor(template_img,cv2.COLOR_BGR2LAB)
+template_mean, template_std = get_mean_and_std(template_img)
+
+# for img in (input_image_list):
+#     print(img)
+    #input_img = cv2.imread(input_dir+img)
+input_img = cv2.imread('road.jpg')    
+input_img = cv2.cvtColor(input_img,cv2.COLOR_BGR2LAB)
+
+
+img_mean, img_std = get_mean_and_std(input_img)
+
+
+height, width, channel = input_img.shape
+for i in range(0,height):
+    for j in range(0,width):
+        for k in range(0,channel):
+            x = input_img[i,j,k]
+            x = ((x-img_mean[k])*(template_std[k]/img_std[k]))+template_mean[k]
+            x = round(x)
+            # boundary check
+            x = 0 if x<0 else x
+            x = 255 if x>255 else x
+            input_img[i,j,k] = x
+        
+input_img= cv2.cvtColor(input_img,cv2.COLOR_LAB2BGR)
+cv2.imwrite("modified.jpg", input_img)