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metrics.py
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metrics.py
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import os
import sys
import numpy as np
import cv2
import math
import pdb
import matplotlib.pyplot as plt
def metrics(original_dir, pred_dir):
# determine length of the sequence (min(in, out))
length = min(len(os.listdir(original_dir)), len(os.listdir(pred_dir)))
image_paths = sorted([path for path in os.listdir(pred_dir) if (path.endswith(".png") and 'mask' not in path)])
# Create brute-force matcher object
bf = cv2.BFMatcher()
sift = cv2.xfeatures2d.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 = []
DV_seq = []
Pt = np.eye(3)
P_seq = []
for i in range(length):
# Load the images in gray scale
img1 = cv2.imread(original_dir + image_paths[i], 0)
img1o = cv2.imread(pred_dir + image_paths[i], 0)
print(original_dir + image_paths[i])
print(pred_dir + image_paths[i])
# 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
if descriptors1 is not None and descriptors1o is not None:
matches = bf.knnMatch(descriptors1, descriptors1o, k=2)
else:
matches = None
if matches is None:
continue
# Select the good matches using the ratio test
goodMatches = []
"""for m, n in matches:
if m.distance < ratio * n.distance:
goodMatches.append(m)"""
for m_n in matches:
if len(m_n) > 1:
m = m_n[0]
n = m_n[1]
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
else:
continue
if M is None:
continue
# 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:
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, _ = cv2.findHomography(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
# print(M)
# M, _ = cv2.estimateAffine2D(sourcePoints, destinationPoints, method=cv2.RANSAC, ransacReprojThreshold=thresh)
#end
else:
continue
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 = []
P_seq_r = []
#pdb.set_trace()
for Mp in P_seq:
#w, _ = np.linalg.eig(Mp[0:2,0:2])
#w = np.sort(w)[::-1]
#DV = w[1]/w[0]
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.append(transRecovered)
P_seq_r.append(thetaRecovered)
if len(P_seq) > 0:
# FFT
fft_t = np.fft.fft(P_seq_t)
fft_r = np.fft.fft(P_seq_r)
# 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[: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)
# Print results
# 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([np.min(CR_seq), 1])) )
print('***Distortion value:')
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 len(CR_seq) > 0:
CRCR = np.min([np.mean(CR_seq), 1])
else:
CRCR = np.nan
if len(DV_seq) > 0:
DVDV = np.absolute(np.min(DV_seq))
else:
DVDV = np.nan
return CRCR, DVDV, (SS_t+SS_r)/2
else:
if len(CR_seq) > 0:
CRCR = np.min([np.mean(CR_seq), 1])
else:
CRCR = np.nan
if len(DV_seq) > 0:
DVDV = np.absolute(np.min(DV_seq))
else:
DVDV = np.nan
return CRCR, DVDV, np.nan