diff --git a/framesEvaluation.py b/framesEvaluation.py
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+++ b/framesEvaluation.py
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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from DFLIMG import DFLJPG
+import glob
+import numpy as np
+import matplotlib.pyplot as plt
+import os, sys, math
+import csv
+import cv2 
+
+
+# In[2]:
+
+
+def normalize_vector(data):
+    return (data - np.min(data)) / (np.max(data) - np.min(data))
+
+def normalize_landmarks(lm):
+    c0 = normalize_vector(lm[:,0])
+    c1 = normalize_vector(lm[:,1])
+    return np.transpose(np.vstack([[c0], [c1]]))
+
+
+# In[3]:
+
+
+landmarks = []
+src_filename = glob.glob('DeepFaceLab_Linux/workspace/data_src/aligned/*.jpg')
+for f in src_filename:
+    img = DFLJPG.load(f)
+    landmarks.append(img.get_landmarks())
+np.save("src_landmarks.npy", landmarks)
+
+
+# In[4]:
+
+
+
+print(len(src_filename))
+print(len(landmarks))
+
+
+# In[5]:
+
+
+
+norm_landmarks = [normalize_landmarks(lm) for lm in landmarks]
+#np.column_stack((NormalizeData(landmarks[0][:,0]), NormalizeData(landmarks[0][:,1])))
+print(len(norm_landmarks))
+
+
+# In[6]:
+
+
+all_src_landmarks = norm_landmarks
+dst_landmarks = []
+
+dists = []
+# This method computes the distance between two images
+# by comparing their landmarks.
+# It calculates the distance between two correspondig landmark's dots
+# (one of the src image, and the other belonging to the dst img)
+# It then sums up all the distances of a pair of images. This sum will be
+# the score assigned to the face swap frame.
+for ii, f in enumerate(glob.glob('DeepFaceLab_Linux/workspace/data_dst/aligned/*.jpg')):
+    img = DFLJPG.load(f)
+    dst_landmarks = normalize_landmarks(img.get_landmarks())
+    dists_local = []
+    for f_src, src_landmarks in enumerate(all_src_landmarks):
+        dist = np.sum(np.linalg.norm(src_landmarks - dst_landmarks, ord=2, axis=1))
+        dists_local.append((dist, f, src_filename[f_src], dst_landmarks, src_landmarks))
+    # get the distance between the dst and the best src image
+    dists.append((sorted(dists_local, key=lambda d: d[0]))[0])
+
+# The Euclidean distance between all the landmarks of two images is 
+# considered as the quality metric used to jugde the resulting frame
+
+# In[7]:
+
+
+# sort all samples from the best to the worst landmark pairing
+dists = list(sorted(dists, key=lambda d: d[0]))
+print("Primo metodo: ", dists[0][0])
+dist_scores = list(zip(*dists))[0]
+
+
+# In[8]:
+
+rangeMax = np.quantile(dist_scores, 0.95)
+fig = plt.figure() # Create matplotlib figure
+plt.hist(dist_scores, bins=500)
+plt.xlabel('Score')
+plt.ylabel('Number of imgs')
+plt.ylim(0, 50)
+plt.axvline(rangeMax, color='red')
+plt.show()
+
+
+# In[9]:
+
+
+print(np.quantile(dist_scores, q=0.95))
+
+# In[10]:
+
+img_index = 0#len(dists)-1
+dst_frame_name = dists[img_index][1]
+dst_frame_num = str(dists[img_index][1])[-10:-6]
+src_frame_name = dists[img_index][2]
+print(dst_frame_name, dst_frame_num, src_frame_name)
+
+
+# In[11]:
+
+
+plt.axis('equal')
+plt.scatter(list(dists[img_index])[4][:, 0], -list(dists[img_index])[4][:, 1], label='src_img')
+
+
+# In[12]:
+
+
+plt.scatter(list(dists[img_index])[3][:, 0], -list(dists[img_index])[3][:, 1])
+plt.plot(list(dists[img_index])[3][:, 0], -list(dists[img_index])[3][:, 1])
+plt.axis('equal')
+
+
+# In[13]:
+
+
+fig = plt.figure()
+ax1 = fig.add_subplot(111)
+ax1.set_aspect('equal')
+
+ax1.scatter(list(dists[img_index])[4][:, 0], -list(dists[img_index])[4][:, 1], label='src_img')
+ax1.scatter(list(dists[img_index])[3][:, 0], -list(dists[img_index])[3][:, 1], c='r', label='dst_img')
+plt.legend(loc='lower left');
+plt.show()
+
+
+# In[14]:
+
+
+# This block computes the exact same evaluation of the beginning block.
+# It's just for debug and check.
+def point_dist(p1, p2):
+    dx, dy = p2[0] - p1[0], p2[1] - p1[1]
+    return math.sqrt(dx*dx + dy*dy)
+src_lm = dists[img_index][3]
+dst_lm = dists[img_index][4]
+plt.axis('equal')
+plt.scatter(src_lm[:,0], -src_lm[:,1], label='src')
+plt.scatter(dst_lm[:,0], -dst_lm[:,1], label='dst')
+sum_dd = 0
+for x1, y1, x2, y2 in zip(src_lm[:,0], -src_lm[:,1], dst_lm[:,0], -dst_lm[:,1]):
+    plt.plot([x1, x2], [y1, y2], c='r')
+    dd = point_dist([x1, y1], [x2, y2])
+    sum_dd += dd
+plt.legend(loc='lower left');
+print(sum_dd, dists[img_index][0], np.sum(np.linalg.norm(src_lm - dst_lm, ord=2, axis=1)))
+
+print("Second metodo: ",sum_dd) 
+
+
+# In[15]:
+
+
+def get_frame(fname, frame_number):
+    cmd = f"ffmpeg -i {fname} -vf select='eq(n\,{frame_number})' -vsync 0 frame_{frame_number}.jpg"
+    os.system(cmd)
+    
+video_fname = '../workspace/result.mp4'
+get_frame(video_fname, dst_frame_num)
+
+
+# In[16]:
+
+
+cmd = 'xdg-open '+dst_frame_name+'&'
+os.system(cmd)
+cmd = 'xdg-open frame_'+dst_frame_num+'.jpg &'
+os.system(cmd)
+print(cmd)
+debug_name = str(dst_frame_name)[0:-12]+"_debug/"+str(dst_frame_name)[-11:-6]+'.jpg'
+print(debug_name)
+cmd = 'xdg-open '+debug_name+'&'
+os.system(cmd)
+cmd = 'xdg-open '+src_frame_name+' &'
+os.system(cmd)
+
+
+# In[31]:
+
+
+dists_w_result = []
+for i, d in enumerate(dists):
+    dists_w_result.append([int(os.path.basename(dists[i][1]).split('_')[0]), d[0] < rangeMax, d[0], d[1],  d[2], "frame_"+str(dists[i][1])[-10:-6]+".jpg", d[3], d[4]])
+print((dists_w_result)[0])
+
+# In[52]:
+
+headers = ["dst_index", "valid_face" , "landmark_distance_sum", "dst_frame_name", "src_frame_name", "result_frame_name", "landmarks"]
+with open('dists.csv', 'w') as v:
+    write = csv.writer(v) 
+    write.writerow(headers)
+    write.writerows(dists_w_result)
+v.close()
+