add file to analyse results

pic_proc/Scripts/read_output_from_Server.py

+import os
+
+import cv2
+import numpy as np
+
+from helpers.image import show_image
+
+path ="/home/guney/Project/Richardson-Lucy-Net/pic_proc/data/output_from_server/"
+a = os.listdir(path)
+a = [path+i for i in a if i.split('.')[-1] == 'npz']
+
+display = False
+
+def var_similarity_multiscale(x,y,s):
+    x = cv2.resize(x, (x.shape[1] // s, x.shape[0] // s))
+    y = cv2.resize(y, (y.shape[1] // s, y.shape[0] // s))
+    return var_similarity(x,y)
+def var_similarity(x,y):
+    cov_m = np.cov(x.flatten(),y.flatten())
+    return cov_m[0,1]/np.sqrt(cov_m[0,0]*cov_m[1,1])
+
+for data in a:
+    data = np.load(data,allow_pickle=True)
+    inference = data["f_x"][0]
+    input = data["x"][0]
+    gt = data["y"][0]
+    if display:
+        show_image(inference,"inference")
+        show_image(gt,"gt")
+        show_image(input,"input")
+
+        # Compute the absolute difference between the images
+        diff = inference - gt
+        diff = np.where(diff<0,0.,diff)
+        _, thresholded_diff = cv2.threshold(diff, 0.1, 1.0, cv2.THRESH_BINARY)
+        thresholded_diff_bgr = cv2.cvtColor(thresholded_diff, cv2.COLOR_GRAY2BGR)
+        thresholded_diff_bgr[np.where((thresholded_diff_bgr == [1.0, 1.0, 1.0]).all(axis=2))] = [0, 0, 1.0]
+
+        diff = gt - inference
+        diff = np.where(diff<0,0.,diff)
+        _, thresholded_diff = cv2.threshold(diff, 0.1, 1.0, cv2.THRESH_BINARY)
+        thresholded_diff_bgr_2 = cv2.cvtColor(thresholded_diff, cv2.COLOR_GRAY2BGR)
+        thresholded_diff_bgr[np.where((thresholded_diff_bgr_2 == [1.0, 1.0, 1.0]).all(axis=2))] += [0, 1.0, 0.0]
+
+        highlighted_image = cv2.addWeighted(cv2.cvtColor(inference, cv2.COLOR_GRAY2BGR), 0.1, thresholded_diff_bgr, 0.9, 0)
+        show_image(highlighted_image,"diff")
+        show_image(cv2.resize(inference, (inference.shape[1] // 2, inference.shape[0] // 2)), "rescaled")
+
+        if cv2.waitKey() == ord('c'):
+            print("exiting")
+            cv2.destroyAllWindows()
+            exit(0)
+    print()
+    for s in range(1,6):
+        print("scale: {} s(gt,f(x)) = {}, s(gt,x) = {}".format(s,var_similarity_multiscale(gt,inference,s),var_similarity_multiscale(gt,input,s)))
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