Push za mene

assignment5/a5_utils.py

@@ -0,0 +1,29 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+def normalize_points(P):
+	# P must be a Nx2 vector of points
+	# first coordinate is x, second is y
+
+	# returns: normalized points in homogeneous coordinates and 3x3 transformation matrix
+
+	mu = np.mean(P, axis=0) # mean
+	scale = np.sqrt(2) / np.mean(np.sqrt(np.sum((P-mu)**2,axis=1))) # scale
+	T = np.array([[scale, 0, -mu[0]*scale],[0, scale, -mu[1]*scale],[0,0,1]]) # transformation matrix
+	P = np.hstack((P,np.ones((P.shape[0],1)))) # homogeneous coordinates
+	res = np.dot(T,P.T).T
+	return res, T
+
+def draw_epiline(l,h,w):
+	# l: line equation (vector of size 3)
+	# h: image height
+	# w: image width
+
+	x0, y0 = map(int, [0, -l[2]/l[1]])
+	x1, y1 = map(int, [w-1, -(l[2]+l[0]*w)/l[1]])
+
+	plt.plot([x0,x1],[y0,y1],'r')
+
+	plt.ylim([0,h])
+	plt.gca().invert_yaxis()

assignment5/data/epipolar/house1_camera.txt

@@ -0,0 +1,3 @@
+  1.6108033e+001  1.3704159e+001 -6.7351564e+001 -1.8838024e+002
+  8.2886212e-001 -6.1257005e+001 -2.7985739e+001 -7.4190016e+000
+  1.6739784e-001 -4.5720139e-002 -8.4811075e-002  5.6548906e-001

assignment5/data/epipolar/house2_camera.txt

@@ -0,0 +1,3 @@
+  1.0571624e+001  4.0812730e+000 -2.2538413e+001 -5.9593366e+001
+  3.1827253e-001 -2.1616617e+001 -9.8820962e+000 -2.7146868e+000
+  6.1142503e-002 -2.0656640e-002 -2.0701037e-002  2.5211789e-001

assignment5/data/epipolar/house_fundamental.txt

@@ -0,0 +1,3 @@
+-0.000000885211824 -0.000005615918803 0.001943109518320
+0.000009392818702 0.000000616883199 -0.012006630150442
+-0.001203084137613 0.011037006977740 -0.085317335867129

assignment5/data/epipolar/house_matches.txt

@@ -0,0 +1,168 @@
+  1.7918200e+002  1.4480400e+002  1.9506100e+002  2.0622700e+002
+  6.1975500e+001  2.5237200e+002  3.8994100e+001  2.3608350e+002
+  3.6931350e+001  1.7577400e+002  4.9915800e+001  1.6315300e+002
+  2.5288250e+002  1.7216700e+002  2.2016750e+002  1.7711100e+002
+  3.2102350e+002  1.4967350e+002  3.0108200e+002  1.5957050e+002
+  2.3981000e+002  2.3064950e+002  2.1114250e+002  2.3486800e+002
+  2.8553800e+002  1.8735650e+002  2.4999600e+002  1.9598200e+002
+  2.8752550e+002  2.1648500e+002  1.7304900e+002  2.4019300e+002
+  8.3516000e+001  1.8413600e+002  8.7022000e+001  1.7447850e+002
+  2.4626850e+002  1.1257100e+002  2.1107700e+002  1.1559400e+002
+  2.8051150e+002  2.2442250e+002  2.4488300e+002  2.3413700e+002
+  2.9222950e+002  1.8776050e+002  2.6047650e+002  1.9700200e+002
+  8.0361000e+001  2.3195900e+002  4.4816200e+001  1.5298600e+002
+  1.3405950e+002  2.1506400e+002  1.1857700e+002  2.0863650e+002
+  1.0150850e+002  1.1811850e+002  1.1154700e+002  1.1312950e+002
+  1.1176850e+002  1.9390400e+002  1.0818650e+002  1.8639750e+002
+  3.1303750e+002  6.3000500e+001  2.8886000e+002  6.6640500e+001
+  3.2170900e+001  2.6592200e+002  1.2461050e+001  2.4410900e+002
+  3.1583150e+002  1.3711850e+002  2.9242350e+002  1.4630450e+002
+  1.2255200e+002  1.5963750e+002  1.2642000e+002  1.5459400e+002
+  2.3903100e+002  1.1067800e+002  2.2058950e+002  1.1328800e+002
+  2.8752550e+002  2.1648500e+002  2.5551700e+002  2.2603200e+002
+  3.1789700e+002  1.2829450e+002  2.9548050e+002  1.3657600e+002
+  6.8518500e+001  1.9854650e+002  6.8640000e+001  1.8656350e+002
+  3.0756150e+002  2.5994000e+002  2.6963400e+002  2.7415950e+002
+  3.0108400e+002  1.7968850e+002  2.7227750e+002  1.9002000e+002
+  2.6805100e+002  2.3139700e+002  1.2610700e+002  1.2922500e+002
+  3.4756300e+002  1.7979550e+002  1.8291800e+002  1.8259850e+002
+  2.8527600e+002  2.1764500e+002  2.5241150e+002  2.2791300e+002
+  3.5513400e+002  1.6721850e+002  3.6814550e+002  1.8297050e+002
+  1.8066250e+002  2.2892400e+002  7.1144500e+001  1.4490950e+002
+  1.8062300e+002  1.9588000e+002  1.6667450e+002  1.9453550e+002
+  2.1837400e+002  2.0376550e+002  1.6342400e+002  1.5180550e+002
+  1.5030000e+002  2.0854100e+002  1.8265850e+002  1.7396650e+002
+  3.2523350e+002  1.4903450e+002  3.0816400e+002  1.6008600e+002
+  2.8740050e+002  2.1190450e+002  2.2859100e+002  1.2327800e+002
+  1.8387500e+002  8.6641500e+001  1.8093250e+002  8.6214500e+001
+  2.4990350e+002  1.1365050e+002  1.2886550e+002  1.3049800e+002
+  1.0095700e+002  1.3465200e+002  1.1176400e+002  1.2888750e+002
+  5.6088000e+001  1.5065400e+002  2.1351100e+002  1.8796300e+002
+  4.2722000e+001  1.7362150e+002  5.5217000e+001  1.6155800e+002
+  2.4575550e+002  1.7222450e+002  1.4366850e+002  6.6007500e+001
+  3.6407250e+002  1.9206950e+002  3.6692550e+002  2.1063000e+002
+  4.1618200e+001  1.7130050e+002  5.5475000e+001  1.5961500e+002
+  2.6041450e+002  2.1894350e+002  2.2666700e+002  2.2586250e+002
+  1.4998800e+002  1.0798350e+002  1.4855600e+002  1.0600400e+002
+  2.0334150e+002  5.3067500e+001  2.0399900e+002  5.3458000e+001
+  9.4893500e+001  1.2663100e+002  1.0741250e+002  1.2088600e+002
+  1.5687100e+002  2.4257150e+002  1.2788050e+002  2.3708150e+002
+  6.1975500e+001  2.5237200e+002  1.0978200e+002  1.0504350e+002
+  2.4169600e+002  1.1238700e+002  2.2238100e+002  1.1529450e+002
+  1.5687100e+002  2.4257150e+002  2.2003500e+002  1.8108050e+002
+  2.8553800e+002  1.8735650e+002  1.6053100e+002  2.2765300e+002
+  2.6596500e+002  2.4005550e+002  2.3147100e+002  2.4844750e+002
+  2.4990350e+002  1.1365050e+002  2.2924850e+002  1.1702150e+002
+  1.6087250e+002  1.4599600e+002  1.5155750e+002  1.4373600e+002
+  2.4626850e+002  1.1257100e+002  2.2645100e+002  1.1562050e+002
+  1.5455250e+002  2.1040350e+002  1.8288300e+002  1.8439900e+002
+  1.0137850e+002  1.3591000e+002  1.8241150e+002  1.5931950e+002
+  1.4433350e+002  2.0504300e+002  1.3153400e+002  1.9987250e+002
+  2.1356300e+002  2.1005050e+002  8.1403000e+001  1.4930350e+002
+  3.0690550e+002  1.3915100e+002  1.6471200e+002  1.4424850e+002
+  1.7242650e+002  2.2057000e+002  1.5083300e+002  2.1785750e+002
+  2.5155300e+002  2.1905500e+002  2.1929050e+002  2.2458700e+002
+  3.4451900e+001  2.2563950e+002  2.8594550e+001  2.0850100e+002
+  1.5030000e+002  2.0854100e+002  1.3601750e+002  2.0423250e+002
+  1.8066250e+002  2.2892400e+002  1.5533050e+002  2.2653700e+002
+  2.3232950e+002  1.1404500e+002  2.1404300e+002  1.1640950e+002
+  2.1356300e+002  2.1005050e+002  1.9165250e+002  2.1161750e+002
+  3.2473050e+002  2.4401900e+002  2.9429500e+002  2.6040700e+002
+  1.5455250e+002  2.1040350e+002  1.3849450e+002  2.0611350e+002
+  2.5407600e+002  2.3183650e+002  2.2241750e+002  2.3866850e+002
+  1.7747850e+002  1.5331750e+002  1.0307000e+002  1.8974250e+002
+  1.1764400e+002  1.3685650e+002  1.2326950e+002  1.3158800e+002
+  3.0529400e+002  1.9526650e+002  1.5803850e+002  2.2521350e+002
+  3.2523350e+002  1.4903450e+002  1.8651150e+002  4.4059250e+001
+  2.9683100e+002  2.0801450e+002  2.6987100e+002  2.1891300e+002
+  6.2727000e+001  1.6241450e+002  7.7038000e+001  1.5270200e+002
+  9.4893500e+001  1.2663100e+002  2.2716350e+002  1.9914500e+002
+  6.2285000e+001  1.8180850e+002  6.9213000e+001  1.7063550e+002
+  3.0235850e+002  1.5133850e+002  2.7282300e+002  1.6042950e+002
+  2.3903100e+002  1.1067800e+002  7.9453000e+001  1.7107700e+002
+  3.4732250e+002  1.8414400e+002  3.5243400e+002  2.0054950e+002
+  2.1410550e+002  2.0812100e+002  1.9209200e+002  2.1014500e+002
+  2.9651650e+002  1.7797850e+002  2.6587100e+002  1.8715900e+002
+  3.0690550e+002  1.3915100e+002  2.7905500e+002  1.4664550e+002
+  3.0755450e+002  1.5028450e+002  2.8154800e+002  1.5912800e+002
+  7.6427500e+001  1.8039550e+002  8.2665500e+001  1.7071850e+002
+  2.3471950e+002  1.1491450e+002  2.1604250e+002  1.1743850e+002
+  2.4169600e+002  1.1238700e+002  6.7750500e+001  1.9952400e+002
+  3.0755450e+002  1.5028450e+002  2.3814250e+002  2.5626200e+002
+  3.1365900e+002  2.0281000e+002  2.9498900e+002  2.1596750e+002
+  2.1411400e+002  1.9151850e+002  1.9161650e+002  1.9293450e+002
+  2.8811050e+002  1.9081650e+002  2.5483200e+002  1.9954550e+002
+  1.2948250e+002  2.2789950e+002  1.0891000e+002  2.2010300e+002
+  8.3516000e+001  1.8413600e+002  1.9472300e+002  1.9885950e+002
+  7.8119000e+001  1.7241850e+002  8.7477000e+001  1.6300700e+002
+  2.9651650e+002  1.7797850e+002  1.8210000e+002  4.8681700e+001
+  1.2255200e+002  1.5963750e+002  1.1189250e+002  1.3000250e+002
+  3.5513400e+002  1.6721850e+002  2.3805150e+002  1.2053600e+002
+  3.2596550e+002  1.5994300e+002  3.1038750e+002  1.7185400e+002
+  1.0853300e+002  1.6344500e+002  1.1633200e+002  1.5656000e+002
+  3.6180050e+002  2.0686900e+002  7.2610500e+001  1.5987150e+002
+  2.0012100e+002  4.5417000e+001  2.0097650e+002  4.5859950e+001
+  1.8665100e+002  7.6156000e+001  1.8557650e+002  7.6297500e+001
+  3.0668800e+002  1.5394750e+002  2.7869700e+002  1.6339550e+002
+  3.3010150e+002  1.5469900e+002  3.1804750e+002  1.6654000e+002
+  3.2102350e+002  1.4967350e+002  1.5227100e+002  1.8614150e+002
+  2.8384950e+001  1.8400600e+002  3.9704350e+001  1.7010550e+002
+  2.9683100e+002  2.0801450e+002  2.2525600e+002  1.9041800e+002
+  6.2074500e+001  2.3851850e+002  4.5835800e+001  2.2259600e+002
+  1.5179600e+002  1.6906650e+002  1.8530350e+002  1.6236650e+002
+  3.3010150e+002  1.5469900e+002  7.0637500e+001  1.5868400e+002
+  1.9101900e+002  2.0624350e+002  1.7409600e+002  2.0558000e+002
+  1.4433350e+002  2.0504300e+002  8.5874500e+001  1.5348500e+002
+  3.2180250e+002  1.3722050e+002  3.0240700e+002  1.4650100e+002
+  3.6793900e+002  2.2950150e+002  3.5223750e+002  2.5125600e+002
+  3.5791800e+002  1.9988800e+002  3.5591800e+002  2.1845550e+002
+  1.9674400e+002  2.0645350e+002  1.7915100e+002  2.0655600e+002
+  3.1816550e+002  1.1693600e+002  2.9533550e+002  1.2460750e+002
+  3.2399950e+002  1.8537150e+002  3.1067200e+002  1.9826250e+002
+  1.1153800e+001  1.5971450e+002  1.7628550e+002  1.6593900e+002
+  3.3210650e+002  1.4851150e+002  3.1965600e+002  1.5978300e+002
+  2.7584500e+002  8.8974500e+001  2.7500850e+002  9.3037500e+001
+  4.1915300e+001  1.8043400e+002  4.3957650e+001  1.6738150e+002
+  3.2624500e+002  1.3943600e+002  3.1023500e+002  1.4940550e+002
+  3.1224400e+002  1.3321800e+002  2.8568650e+002  1.4196700e+002
+  2.9726900e+002  1.9775700e+002  2.6840300e+002  2.0876500e+002
+  5.6088000e+001  1.5065400e+002  7.6481000e+001  1.4130750e+002
+  1.9101900e+002  2.0624350e+002  1.0835400e+002  1.7635550e+002
+  3.0365650e+002  1.5001600e+002  2.7446550e+002  1.5841800e+002
+  1.7912700e+002  1.5299450e+002  2.3346650e+002  1.4753300e+002
+  2.9222950e+002  1.8776050e+002  5.8090000e+001  1.6222400e+002
+  3.2479850e+002  1.3931850e+002  3.0716100e+002  1.4911300e+002
+  1.3144600e+002  6.8035000e+001  3.5574450e+002  2.1173050e+002
+  3.1789700e+002  1.2829450e+002  2.1445850e+002  1.7648300e+002
+  8.0361000e+001  2.3195900e+002  6.4276000e+001  2.1868200e+002
+  3.6180050e+002  2.0686900e+002  3.5478100e+002  2.2578000e+002
+  9.6984500e+001  1.3567950e+002  1.0974850e+002  1.2934550e+002
+  3.4747600e+002  2.2390200e+002  3.3133850e+002  2.4183450e+002
+  1.1153800e+001  1.5971450e+002  3.5802700e+001  1.4647600e+002
+  2.7404450e+002  2.4653600e+002  1.6532900e+002  1.5169450e+002
+  3.0529400e+002  1.9526650e+002  2.8159550e+002  2.0687550e+002
+  5.7455500e+001  1.6895950e+002  2.1473250e+002  1.0983800e+002
+  1.2769600e+002  1.9505900e+002  1.2123950e+002  1.8901250e+002
+  2.6053200e+001  1.6551050e+002  1.9648950e+002  2.0448800e+002
+  2.8740050e+002  2.1190450e+002  2.5464750e+002  2.2193400e+002
+  2.5593700e+002  2.2407050e+002  2.2306950e+002  2.3054600e+002
+  2.3471950e+002  1.1491450e+002  2.1934950e+002  2.0089500e+002
+  3.1959300e+002  1.5739450e+002  3.0088450e+002  1.6808900e+002
+  1.9238350e+002  1.6535150e+002  1.0695850e+002  1.1564550e+002
+  2.4247550e+002  2.3166200e+002  2.1305000e+002  2.3636450e+002
+  3.0218800e+002  1.7132600e+002  2.7425350e+002  1.8068600e+002
+  1.7601850e+002  1.4352700e+002  1.6290100e+002  1.4236350e+002
+  3.0855650e+002  1.4262350e+002  2.8091400e+002  1.5121450e+002
+  3.1638550e+002  1.5340300e+002  2.9527450e+002  1.6362750e+002
+  2.5261200e+002  1.2029950e+002  2.3018150e+002  1.2391750e+002
+  1.7568950e+002  2.2714250e+002  1.5119350e+002  2.2430800e+002
+  1.5179600e+002  1.6906650e+002  1.4629400e+002  1.6549950e+002
+  1.9238350e+002  1.6535150e+002  1.7466150e+002  1.6517000e+002
+  3.1037750e+002  2.5751600e+002  5.8449500e+001  1.5756250e+002
+  2.6087750e+002  1.9306100e+002  1.2478950e+002  1.3764900e+002
+  7.3081000e+001  1.6900550e+002  8.4426500e+001  1.5967050e+002
+  2.8041600e+002  2.0380450e+002  2.4273700e+002  2.1295550e+002
+  3.4756300e+002  1.7979550e+002  3.5326600e+002  1.9526800e+002
+  2.5652100e+002  1.7681900e+002  2.2275200e+002  1.8204100e+002
+  3.1037750e+002  2.5751600e+002  2.7346600e+002  2.7244450e+002
+  2.6805100e+002  2.3139700e+002  2.3297150e+002  2.3981050e+002

assignment5/data/epipolar/house_points.txt

@@ -0,0 +1,10 @@
+1.9220093e+002 4.4911215e+001 1.9011120e+002 4.6260498e+001
+3.2319159e+002 6.4051402e+001 2.9641291e+002 6.8954121e+001
+1.3238785e+002 6.8836449e+001 1.4352955e+002 6.7162519e+001
+3.1302336e+002 1.1250000e+002 2.8566330e+002 1.2031337e+002
+1.0367757e+002 1.1010748e+002 1.1187792e+002 1.0478616e+002
+2.7713551e+002 1.7051869e+002 2.3848445e+002 1.7645023e+002
+8.7528037e+001 1.5317290e+002 1.0232271e+002 1.4479860e+002
+2.7593925e+002 2.4588318e+002 2.3967885e+002 2.5647512e+002
+3.2139720e+002 2.0999533e+002 3.1134292e+002 2.2542068e+002
+2.8132243e+002 1.9264953e+002 2.4385925e+002 1.9974106e+002

assignment5/solution.py

@@ -0,0 +1,67 @@
+import numpy as np
+import numpy.typing as npt
+from matplotlib import pyplot as plt
+import cv2
+import uz_framework.image as uz_image
+import uz_framework.text as uz_text
+import os
+
+##############################################
+# EXCERCISE 1: Exercise 1: Image derivatives #
+##############################################
+
+def ex1():
+	one_b()
+
+def one_a() -> None:
+	"""
+	d = (f * px)/pz + f(T - px)/pz => d = (f * T) / pz
+	"""
+
+def one_b() -> None:
+	"""
+	Write a script that computes the disparity for a range of values of pz. Plot the values
+	to a figure and set the appropriate units to axes. Use the following parameters of
+	the system: focal length is f = 2.5mm and stereo system baseline is T = 12cm
+	"""
+
+	pz_values = np.arange(0.001, 0.1, 0.001)
+	f = 0.0025 
+	T = 0.12
+
+	d = (f * T) / pz_values
+
+	plt.plot(pz_values, d)
+
+	plt.xlabel('pz [m]')
+	plt.ylabel('d [m]')
+	plt.title('Disparity for a range of values of pz')
+	plt.show()
+
+def one_c() -> None:
+	"""
+	In order to get a better grasp on the idea of distance and disparity, you will calculate
+	the numbers for a specific case. We will take the parameters from a specification of
+	a commercial stereo camera Bumblebee2 manufactured by the company PointGray:
+	f = 2.5mm, T = 12cm, whose image sensor has a resolution of 648×488 pixels that
+	are square and the width of a pixel is 7.4µm. We assume that there is no empty
+	space between pixels and that both cameras are completely parallel and equal. Lets
+	say that we use this system to observe a (point) object that is detected at pixel 550
+	in x axis in the left camera and at the pixel 300 in the right camera. How far is the
+	object (in meters) in this case? How far is the object if the object is detected at
+	pixel 540 in the right camera? Solve this task analytically and bring your solution
+	to the presentation of the exercise.
+	"""
+
+
+# ######## #
+# SOLUTION #
+# ######## #
+
+def main():
+	ex1() 
+	#ex2()
+	#ex3()
+
+if __name__ == '__main__':
+	main()

assignment5/uz_framework/text.py

@@ -0,0 +1,9 @@
+import numpy as np
+
+def read_data(filename: str):
+    # reads a numpy array from a text file
+    with open(filename) as f:
+        s = f.read()
+
+    return np.fromstring(s, sep=' ')
+