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Merge pull request #7 from stefanklut/master
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Fixing homography decomposition
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@jinsc37
jinsc37 authored Apr 16, 2021
2 parents acb3fef + a3fe0e5 commit 8c9ff9b
Showing 1 changed file with 98 additions and 96 deletions.
194 changes: 98 additions & 96 deletions metrics.py
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Original file line number Diff line number Diff line change
@@ -1,44 +1,89 @@
# Based on: https://github.com/jinsc37/DIFRINT/blob/master/metrics.py

import os
import sys
import numpy as np
import cv2
import math
import pdb
import matplotlib.pyplot as plt

def metrics(in_src, out_src):
frameList = os.listdir(out_src)
frameList.sort()
def metrics(original_dir, pred_dir):
image_paths = sorted([path for path in os.listdir(pred_dir) if path.endswith(".png")])

# Create brute-force matcher object
bf = cv2.BFMatcher()

sift = cv2.SIFT_create()

# Apply the homography transformation if we have enough good matches
MIN_MATCH_COUNT = 10 #10

ratio = 0.7 #0.7
thresh = 5.0 #5.0

CR_seq = np.asarray([1])
DV_seq = np.asarray([1])
Pt = np.asarray([[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0]])
CR_seq = []
DV_seq = []
Pt = np.eye(3)
P_seq = []
count = 1
for f in frameList:
if f.endswith('.png'):
# Load the images in gray scale
img1 = cv2.imread(in_src + f, 0)
img1o = cv2.imread(out_src + f, 0)

# Detect the SIFT key points and compute the descriptors for the two images
sift = cv2.xfeatures2d.SURF_create()
keyPoints1, descriptors1 = sift.detectAndCompute(img1, None)
keyPoints1o, descriptors1o = sift.detectAndCompute(img1o, None)

# Match the descriptors
matches = bf.knnMatch(descriptors1, descriptors1o, k=2)

# Select the good matches using the ratio test

for i in range(len(image_paths)):
# Load the images in gray scale
img1 = cv2.imread(original_dir + image_paths[i], 0)
img1o = cv2.imread(pred_dir + image_paths[i], 0)

# Detect the SIFT key points and compute the descriptors for the two images
keyPoints1, descriptors1 = sift.detectAndCompute(img1, None)
keyPoints1o, descriptors1o = sift.detectAndCompute(img1o, None)

# Match the descriptors
matches = bf.knnMatch(descriptors1, descriptors1o, k=2)

# Select the good matches using the ratio test
goodMatches = []

for m, n in matches:
if m.distance < ratio * n.distance:
goodMatches.append(m)

if len(goodMatches) > MIN_MATCH_COUNT:
# Get the good key points positions
sourcePoints = np.float32([ keyPoints1[m.queryIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)
destinationPoints = np.float32([ keyPoints1o[m.trainIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)

# Obtain the homography matrix
M, _ = cv2.findHomography(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
# M, _ = cv2.estimateAffine2D(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
#end

# Obtain Scale, Translation, Rotation, Distortion value

# WRONG This is not the scale
# sx = M[0, 0]
# sy = M[1, 1]
# scaleRecovered = np.sqrt(sx*sy)

# Based on https://math.stackexchange.com/questions/78137/decomposition-of-a-nonsquare-affine-matrix
scaleRecovered = np.sqrt(M[0,1]**2 + M[0,0]**2)
# scalexRecovered = np.sqrt(M[0,0]**2 + M[1,0]**2)
# scaleyRecovered = np.sqrt(M[0,1]**2 + M[1,1]**2)
# scaleRecovered = np.sqrt(scalexRecovered**2 + scaleyRecovered**2)


# WRONG This is not the affine part right?
w, _ = np.linalg.eig(M[0:2, 0:2])
# w, _ = np.linalg.eig(M[0:2])
w = np.sort(w)[::-1]
DV = w[1]/w[0]
#pdb.set_trace()

CR_seq.append(1/scaleRecovered)
DV_seq.append(DV)

# For Stability score calculation
if i+1 < len(image_paths):

img2o = cv2.imread(pred_dir + image_paths[i+1], 0)

keyPoints2o, descriptors2o = sift.detectAndCompute(img2o, None)
matches = bf.knnMatch(descriptors1o, descriptors2o, k=2)
goodMatches = []

for m, n in matches:
Expand All @@ -47,113 +92,70 @@ def metrics(in_src, out_src):

if len(goodMatches) > MIN_MATCH_COUNT:
# Get the good key points positions
sourcePoints = np.float32([ keyPoints1[m.queryIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)
destinationPoints = np.float32([ keyPoints1o[m.trainIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)
sourcePoints = np.float32([ keyPoints1o[m.queryIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)
destinationPoints = np.float32([ keyPoints2o[m.trainIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)

# Obtain the homography matrix
M, mask = cv2.findHomography(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
M, _ = cv2.findHomography(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
# print(M)
# M, _ = cv2.estimateAffine2D(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
#end

# Obtain Scale, Translation, Rotation, Distortion value
sx = M[0, 0]
sy = M[1, 1]
scaleRecovered = math.sqrt(sx*sy)

w, _ = np.linalg.eig(M[0:2,0:2])
w = np.sort(w)[::-1]
DV = w[1]/w[0]
#pdb.set_trace()

CR_seq = np.concatenate((1.0/CR_seq, [scaleRecovered]), axis=0)
DV_seq = np.concatenate((DV_seq, [DV]), axis=0)



# For Stability score calculation
if count < len(frameList):
img2o = cv2.imread(out_src + f[:-9] + '%05d.png' % (int(f[-9:-4])+1), 0)

keyPoints2o, descriptors2o = sift.detectAndCompute(img2o, None)
matches = bf.knnMatch(descriptors1o, descriptors2o, k=2)
goodMatches = []

for m, n in matches:
if m.distance < ratio * n.distance:
goodMatches.append(m)

if len(goodMatches) > MIN_MATCH_COUNT:
# Get the good key points positions
sourcePoints = np.float32([ keyPoints1o[m.queryIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)
destinationPoints = np.float32([ keyPoints2o[m.trainIdx].pt for m in goodMatches ]).reshape(-1, 1, 2)

# Obtain the homography matrix
M, mask = cv2.findHomography(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
#end

P_seq.append(np.matmul(Pt, M))
Pt = np.matmul(Pt, M)
sys.stdout.write('\rFrame: ' + str(count) + '/' + str(len(frameList)))
sys.stdout.flush()
count += 1
P_seq.append(np.matmul(Pt, M))
Pt = np.matmul(Pt, M)
sys.stdout.write('\rFrame: ' + str(i) + '/' + str(len(image_paths)))
sys.stdout.flush()
#end
#end

# Make 1D temporal signals
P_seq_t = np.asarray([1])
P_seq_r = np.asarray([1])

P_seq_t = []
P_seq_r = []
#pdb.set_trace()
for Mp in P_seq:
sx = Mp[0, 0]
sy = Mp[1, 1]
c = Mp[0, 2]
f = Mp[1, 2]
#w, _ = np.linalg.eig(Mp[0:2,0:2])
#w = np.sort(w)[::-1]
#DV = w[1]/w[0]
transRecovered = math.sqrt(c*c + f*f)
thetaRecovered = math.atan2(sx, sy) * 180 / math.pi
transRecovered = np.sqrt(Mp[0, 2]**2 + Mp[1, 2]**2)
# Based on https://math.stackexchange.com/questions/78137/decomposition-of-a-nonsquare-affine-matrix
thetaRecovered = np.arctan2(Mp[1, 0], Mp[0, 0]) * 180 / np.pi
#thetaRecovered = DV
P_seq_t = np.concatenate((P_seq_t, [transRecovered]), axis=0)
P_seq_r = np.concatenate((P_seq_r, [thetaRecovered]), axis=0)

P_seq_t = np.delete(P_seq_t, 0)
P_seq_r = np.delete(P_seq_r, 0)
P_seq_t.append(transRecovered)
P_seq_r.append(thetaRecovered)

# FFT
fft_t = np.fft.fft(P_seq_t)
fft_r = np.fft.fft(P_seq_r)
fft_t = abs(fft_t)**2
fft_r = abs(fft_r)**2
# WRONG What is this for?
fft_t = np.abs(fft_t)**2
fft_r = np.abs(fft_r)**2

#freq = np.fft.fftfreq(len(P_seq_t))
#plt.plot(freq, abs(fft_r)**2)
#plt.show()
#print(abs(fft_r)**2)
#print(freq)
fft_t = np.delete(fft_t, 0)
fft_r = np.delete(fft_r, 0)
fft_t = fft_t[:int(len(fft_t)/2)]
fft_r = fft_r[:int(len(fft_r)/2)]
fft_t = fft_t[:len(fft_t)//2]
fft_r = fft_r[:len(fft_r)//2]

SS_t = np.sum(fft_t[:5])/np.sum(fft_t)
SS_r = np.sum(fft_r[:5])/np.sum(fft_r)

# Delete initialized entry
CR_seq = np.delete(CR_seq, 0)
DV_seq = np.delete(DV_seq, 0)

# Print results
print('\n***Last H:')
print(M)
# print('\n***Last H:')
# print(M)
print('\n')
print('***Cropping ratio (Avg, Min):')
print( str.format('{0:.4f}', np.min([np.mean(CR_seq), 1])) +' | '+ str.format('{0:.4f}', np.min([CR_seq.min(), 1])) )
print( str.format('{0:.4f}', np.min([np.mean(CR_seq), 1])) +' | '+ str.format('{0:.4f}', np.min([np.min(CR_seq), 1])) )
print('***Distortion value:')
print(str.format('{0:.4f}', np.absolute(DV_seq.min())) )
print(str.format('{0:.4f}', np.absolute(np.min(DV_seq))) )
print('***Stability Score (Avg, Trans, Rot):')
print(str.format('{0:.4f}', (SS_t+SS_r)/2) +' | '+ str.format('{0:.4f}', SS_t) +' | '+ str.format('{0:.4f}', SS_r) )

if __name__ == '__main__':
metrics(in_src='./data/Stab_te_reg/07/', out_src='./output/OurStabReg2/07/')



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