branch dev tout marche pas, test12 and paramgui

calib.py

@@ -0,0 +1,85 @@
+
+#retval, corners = cv2.findChessboardCorners(image,patternSize, flags)
+
+import cv2
+import numpy as np
+
+# Define the size of the chessboard
+chessboard_size = (8,5)
+
+# Define the object points of the chessboard
+object_points = np.zeros((np.prod(chessboard_size), 3), dtype=np.float32)
+object_points[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2)
+
+# Create arrays to store the object points and image points from all the images
+object_points_array = []
+image_points_array1 = []
+image_points_array2 = []
+
+# Load the images
+images = []
+images.append(cv2.imread("/home/ros/Bureau/ca_ur5/1.jpg"))
+# Add more images as needed
+
+# Loop through each image and find the chessboard corners
+for image in images:
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Find the chessboard corners
+    found, corners = cv2.findChessboardCorners(gray, chessboard_size, None)
+
+    # If the corners are found, add the object points and image points to the arrays
+    if found:
+        object_points_array.append(object_points)
+        image_points_array1.append(corners)
+
+# Calibrate the camera using the object points and image points
+ret, camera_matrix, distortion_coefficients, rotation_vectors, translation_vectors = cv2.calibrateCamera(
+    object_points_array, image_points_array1, gray.shape[::-1], None, None)
+
+# Print the camera matrix and distortion coefficients
+print("Camera matrix:")
+print(camera_matrix)
+print("Distortion coefficients:")
+print(distortion_coefficients)
+
+
+# Load the images
+images = []
+images.append(cv2.imread("/home/ros/Bureau/ca_ur5/2.jpg"))
+# Add more images as needed
+
+# Loop through each image and find the chessboard corners
+for image in images:
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Find the chessboard corners
+    found, corners = cv2.findChessboardCorners(gray, chessboard_size, None)
+
+    # If the corners are found, add the object points and image points to the arrays
+    if found:
+        object_points_array.append(object_points)
+        image_points_array2.append(corners)
+
+# Calibrate the camera using the object points and image points
+ret, camera_matrix, distortion_coefficients, rotation_vectors, translation_vectors = cv2.calibrateCamera(
+    object_points_array, image_points_array2, gray.shape[::-1], None, None)
+
+# Print the camera matrix and distortion coefficients
+print("Camera matrix:")
+print(camera_matrix)
+print("Distortion coefficients:")
+print(distortion_coefficients)
+
+print("Stereo calib")
+
+flags = 0
+flags |= cv2.CALIB_FIX_INTRINSIC
+# Here we fix the intrinsic camara matrixes so that only Rot, Trns, Emat and Fmat are calculated.
+# Hence intrinsic parameters are the same 
+ 
+criteria_stereo= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+ 
+ 
+# This step is performed to transformation between the two cameras and calculate Essential and Fundamenatl matrix
+retS, new_mtxL, distL, new_mtxR, distR, Rot, Trns, Emat, Fmat = cv2.stereoCalibrate(object_points_array, image_points_array1, image_points_array2, new_mtxL, distL, new_mtxR, distR, imgL_gray.shape[::-1], criteria_stereo, flags)

calibonelib.py

@@ -0,0 +1,54 @@
+# Set the path to the images captured by the left and right cameras
+import cv2
+import numpy as np
+import tqdm as tqdm
+
+pathL = "/home/ros/Bureau/ca_ur5/1.jpg"
+pathR = "/home/ros/Bureau/ca_ur5/2.jpg"
+ 
+# Termination criteria for refining the detected corners
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+ 
+ 
+objp = np.zeros((8*5,3), np.float32)
+objp[:,:2] = np.mgrid[0:8,0:5].T.reshape(-1,2)
+ 
+img_ptsL = []
+img_ptsR = []
+obj_pts = []
+ 
+for i in tqdm(range(1,12)):
+  imgL = cv2.imread(pathL+"img%d.png"%i)
+  imgR = cv2.imread(pathR+"img%d.png"%i)
+  imgL_gray = cv2.imread(pathL+"img%d.png"%i,0)
+  imgR_gray = cv2.imread(pathR+"img%d.png"%i,0)
+ 
+  outputL = imgL.copy()
+  outputR = imgR.copy()
+ 
+  retR, cornersR =  cv2.findChessboardCorners(outputR,(8,5),None)
+  retL, cornersL = cv2.findChessboardCorners(outputL,(8,5),None)
+ 
+  if retR and retL:
+    obj_pts.append(objp)
+    cv2.cornerSubPix(imgR_gray,cornersR,(11,11),(-1,-1),criteria)
+    cv2.cornerSubPix(imgL_gray,cornersL,(11,11),(-1,-1),criteria)
+    cv2.drawChessboardCorners(outputR,(8,5),cornersR,retR)
+    cv2.drawChessboardCorners(outputL,(8,5),cornersL,retL)
+    cv2.imshow('cornersR',outputR)
+    cv2.imshow('cornersL',outputL)
+    cv2.waitKey(0)
+ 
+    img_ptsL.append(cornersL)
+    img_ptsR.append(cornersR)
+ 
+ 
+# Calibrating left camera
+retL, mtxL, distL, rvecsL, tvecsL = cv2.calibrateCamera(obj_pts,img_ptsL,imgL_gray.shape[::-1],None,None)
+hL,wL= imgL_gray.shape[:2]
+new_mtxL, roiL= cv2.getOptimalNewCameraMatrix(mtxL,distL,(wL,hL),1,(wL,hL))
+ 
+# Calibrating right camera
+retR, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera(obj_pts,img_ptsR,imgR_gray.shape[::-1],None,None)
+hR,wR= imgR_gray.shape[:2]
+new_mtxR, roiR= cv2.getOptimalNewCameraMatrix(mtxR,distR,(wR,hR),1,(wR,hR))

calibrate.py

@@ -13,14 +13,14 @@ print("Extracting image coordinates of respective 3D pattern ....\n")
 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
 
 
-objp = np.zeros((21*15,3), np.float32)
+objp = np.zeros((8*5,3), np.float32)
-objp[:,:2] = np.mgrid[0:21,0:15].T.reshape(-1,2)
+objp[:,:2] = np.mgrid[0:8,0:5].T.reshape(-1,2)
 
 img_ptsL = []
 img_ptsR = []
 obj_pts = []
 
-for i in tqdm(range(1,6)): #number of images
+for i in tqdm(range(1,5)):
 	imgL = cv2.imread(pathL+"L%d.jpg"%i)
 	imgR = cv2.imread(pathR+"R%d.jpg"%i)
 	imgL_gray = cv2.imread(pathL+"L%d.jpg"%i,0)
@@ -30,22 +30,16 @@ for i in tqdm(range(1,6)): #number of images
 	outputL = imgL.copy()
 	outputR = imgR.copy()
 
-
+	retR, cornersR =  cv2.findChessboardCorners(outputR,(8,5),None)
-	retR, cornersR =  cv2.findChessboardCorners(outputR,(21,15),None)
+	retL, cornersL = cv2.findChessboardCorners(outputL,(8,5),None)
-	retL, cornersL = cv2.findChessboardCorners(outputL,(21,15),None)
-	print("ret R is",retR)
-	print("ret L is",retL)
-
-	
-	
 
 	if retR and retL:
 		
 		obj_pts.append(objp)
 		cv2.cornerSubPix(imgR_gray,cornersR,(11,11),(-1,-1),criteria)
 		cv2.cornerSubPix(imgL_gray,cornersL,(11,11),(-1,-1),criteria)
-		cv2.drawChessboardCorners(outputR,(21,15),cornersR,retR)
+		cv2.drawChessboardCorners(outputR,(8,5),cornersR,retR)
-		cv2.drawChessboardCorners(outputL,(21,15),cornersL,retL)
+		cv2.drawChessboardCorners(outputL,(8,5),cornersL,retL)
 		cv2.imshow('cornersR',outputR)
 		cv2.imshow('cornersL',outputL)
 		cv2.waitKey(0)

data/depth_estmation_params_py.xml

@@ -1,15 +0,0 @@
-<?xml version="1.0"?>
-<opencv_storage>
-<blockSize>15</blockSize>
-<numDisparities>16</numDisparities>
-<preFilterType>1</preFilterType>
-<preFilterSize>9</preFilterSize>
-<preFilterCap>5</preFilterCap>
-<textureThreshold>10</textureThreshold>
-<uniquenessRatio>15</uniquenessRatio>
-<speckleRange>0</speckleRange>
-<speckleWindowSize>6</speckleWindowSize>
-<disp12MaxDiff>5</disp12MaxDiff>
-<minDisparity>5</minDisparity>
-<M>3.9075000000000003e+01</M>
-</opencv_storage>

dispa2depth.py

@@ -1,210 +0,0 @@
-import numpy as np 
-import cv2
-import matplotlib
-import matplotlib.pyplot as plt
-
-
-# Check for left and right camera IDs
-# These values can change depending on the system
-CamL_id = 2 # Camera ID for left camera
-CamR_id = 0 # Camera ID for right camera
-
-#CamL= cv2.VideoCapture(CamL_id)
-#CamR= cv2.VideoCapture(CamR_id)
-retL, imgL= cv2.VideoCapture(CamL_id, cv2.CAP_V4L2).read()
-retR, imgR= cv2.VideoCapture(CamR_id, cv2.CAP_V4L2).read()
-'''
-imgR_gray = cv2.cvtColor(imgR,cv2.COLOR_BGR2GRAY)
-imgL_gray = cv2.cvtColor(imgL,cv2.COLOR_BGR2GRAY)
-'''
-
-# Reading the mapping values for stereo image rectification
-cv_file = cv2.FileStorage("data/params_py.xml", cv2.FILE_STORAGE_READ)
-Left_Stereo_Map_x = cv_file.getNode("Left_Stereo_Map_x").mat()
-#print(Left_Stereo_Map_x)
-Left_Stereo_Map_y = cv_file.getNode("Left_Stereo_Map_y").mat()
-Right_Stereo_Map_x = cv_file.getNode("Right_Stereo_Map_x").mat()
-Right_Stereo_Map_y = cv_file.getNode("Right_Stereo_Map_y").mat()
-cv_file.release()
-
-# These parameters can vary according to the setup
-# Keeping the target object at max_dist we store disparity values
-# after every sample_delta distance.
-max_dist = 230 # max distance to keep the target object (in cm)
-min_dist = 50 # Minimum distance the stereo setup can measure (in cm)
-sample_delta = 40 # Distance between two sampling points (in cm)
-
-Z = max_dist 
-Value_pairs = []
-
-disp_map = np.zeros((600,600,3))
-
-
-# Reading the stored the StereoBM parameters
-cv_file = cv2.FileStorage("../data/depth_estmation_params_py.xml", cv2.FILE_STORAGE_READ)
-numDisparities = int(cv_file.getNode("numDisparities").real())
-blockSize = int(cv_file.getNode("blockSize").real())
-preFilterType = int(cv_file.getNode("preFilterType").real())
-preFilterSize = int(cv_file.getNode("preFilterSize").real())
-preFilterCap = int(cv_file.getNode("preFilterCap").real())
-textureThreshold = int(cv_file.getNode("textureThreshold").real())
-uniquenessRatio = int(cv_file.getNode("uniquenessRatio").real())
-speckleRange = int(cv_file.getNode("speckleRange").real())
-speckleWindowSize = int(cv_file.getNode("speckleWindowSize").real())
-disp12MaxDiff = int(cv_file.getNode("disp12MaxDiff").real())
-minDisparity = int(cv_file.getNode("minDisparity").real())
-M = cv_file.getNode("M").real()
-cv_file.release()
-# Defining callback functions for mouse events
-def mouse_click(event,x,y,flags,param):
-	global Z
-	if event == cv2.EVENT_LBUTTONDBLCLK:
-		if disparity[y,x] > 0:
-			Value_pairs.append([Z,disparity[y,x]])
-			print("Distance: %r cm  | Disparity: %r"%(Z,disparity[y,x]))
-			Z-=sample_delta
-			
-
-
-cv2.namedWindow('disp',cv2.WINDOW_NORMAL)
-cv2.resizeWindow('disp',600,600)
-cv2.namedWindow('left image',cv2.WINDOW_NORMAL)
-cv2.resizeWindow('left image',600,600)
-cv2.setMouseCallback('disp',mouse_click)
-
-# Creating an object of StereoBM algorithm
-stereo = cv2.StereoBM_create()
-
-while True:
-
-	# Capturing and storing left and right camera images
-	retL, imgL= cv2.VideoCapture(CamL_id, cv2.CAP_V4L2).read()
-	retR, imgR= cv2.VideoCapture(CamR_id, cv2.CAP_V4L2).read()
-	'''
-	cv2.imshow('imgL',imgL)
-	cv2.waitKey(0)
-	
-	retR, imgR= CamR.read()
-	retL, imgL= CamL.read()
-	'''
-	# Proceed only if the frames have been captured
-	if retL and retR:
-		imgR_gray = cv2.cvtColor(imgR,cv2.COLOR_BGR2GRAY)
-		imgL_gray = cv2.cvtColor(imgL,cv2.COLOR_BGR2GRAY)
-		
-		'''
-		cv2.imshow('imgL_gray',imgL_gray)
-		cv2.imshow('imgR_gray',imgR_gray)
-		cv2.waitKey(0)
-		'''
-		
-		
-		# Applying stereo image rectification on the left image
-		Left_nice= cv2.remap(imgL_gray,
-							Left_Stereo_Map_x,
-							Left_Stereo_Map_y,
-							cv2.INTER_LANCZOS4,
-							cv2.BORDER_CONSTANT,
-							0)
-		cv2.imshow('imgL nice',Left_nice)
-		cv2.waitKey(0)
-		#print(Left_nice)
-		# Applying stereo image rectification on the right image
-		Right_nice= cv2.remap(imgR_gray,
-							Right_Stereo_Map_x,
-							Right_Stereo_Map_y,
-							cv2.INTER_LANCZOS4,
-							cv2.BORDER_CONSTANT,
-							0)
-		cv2.imshow('imgR nice',Right_nice)
-		cv2.waitKey(0)		
-		
-		# Setting the updated parameters before computing disparity map
-		stereo.setNumDisparities(numDisparities)
-		stereo.setBlockSize(blockSize)
-		stereo.setPreFilterType(preFilterType)
-		stereo.setPreFilterSize(preFilterSize)
-		stereo.setPreFilterCap(preFilterCap)
-		stereo.setTextureThreshold(textureThreshold)
-		stereo.setUniquenessRatio(uniquenessRatio)
-		stereo.setSpeckleRange(speckleRange)
-		stereo.setSpeckleWindowSize(speckleWindowSize)
-		stereo.setDisp12MaxDiff(disp12MaxDiff)
-		stereo.setMinDisparity(minDisparity)
-
-		# Calculating disparity using the StereoBM algorithm
-		disparity = stereo.compute(Left_nice,Right_nice)
-		# NOTE: compute returns a 16bit signed single channel image,
-		# CV_16S containing a disparity map scaled by 16. Hence it 
-		# is essential to convert it to CV_16S and scale it down 16 times.
-
-		# Converting to float32 
-		disparity = disparity.astype(np.float32)
-
-		# Scaling down the disparity values and normalizing them 
-		disparity = (disparity/16.0 - minDisparity)/numDisparities
-
-		# Displaying the disparity map
-		cv2.imshow("disp",disparity)
-		cv2.imshow("left image",imgL)
-
-		if cv2.waitKey(1) == 27:
-			break
-		
-		if Z < min_dist:
-			break
-	
-	else:
-		print("on est dans le else")
-		'''
-		CamL= cv2.VideoCapture(CamL_id)
-		CamR= cv2.VideoCapture(CamR_id)
-		'''
-
-# solving for M in the following equation
-# ||    depth = M * (1/disparity)   ||
-# for N data points coeff is Nx2 matrix with values 
-# 1/disparity, 1
-# and depth is Nx1 matrix with depth values
-
-value_pairs = np.array(Value_pairs)
-z = value_pairs[:,0]
-disp = value_pairs[:,1]
-disp_inv = 1/disp
-
-# Plotting the relation depth and corresponding disparity
-fig, (ax1,ax2) = plt.subplots(1,2,figsize=(12,6))
-ax1.plot(disp, z, 'o-')
-ax1.set(xlabel='Normalized disparity value', ylabel='Depth from camera (cm)',
-       title='Relation between depth \n and corresponding disparity')
-ax1.grid()
-ax2.plot(disp_inv, z, 'o-')
-ax2.set(xlabel='Inverse disparity value (1/disp) ', ylabel='Depth from camera (cm)',
-       title='Relation between depth \n and corresponding inverse disparity')
-ax2.grid()
-plt.show()
-
-
-# Solving for M using least square fitting with QR decomposition method
-coeff = np.vstack([disp_inv, np.ones(len(disp_inv))]).T
-ret, sol = cv2.solve(coeff,z,flags=cv2.DECOMP_QR)
-M = sol[0,0]
-C = sol[1,0]
-print("Value of M = ",M)
-
-
-# Storing the updated value of M along with the stereo parameters
-cv_file = cv2.FileStorage("../data/depth_estmation_params_py.xml", cv2.FILE_STORAGE_WRITE)
-cv_file.write("numDisparities",numDisparities)
-cv_file.write("blockSize",blockSize)
-cv_file.write("preFilterType",preFilterType)
-cv_file.write("preFilterSize",preFilterSize)
-cv_file.write("preFilterCap",preFilterCap)
-cv_file.write("textureThreshold",textureThreshold)
-cv_file.write("uniquenessRatio",uniquenessRatio)
-cv_file.write("speckleRange",speckleRange)
-cv_file.write("speckleWindowSize",speckleWindowSize)
-cv_file.write("disp12MaxDiff",disp12MaxDiff)
-cv_file.write("minDisparity",minDisparity)
-cv_file.write("M",M)
-cv_file.release()

disparityParam_gui.py

@@ -4,47 +4,43 @@ import cv2
 
 # Check for left and right camera IDs
 # These values can change depending on the system
-CamL_id = 0# Camera ID for left camera
+CamL_id = 6# Camera ID for left camera
-CamR_id = 2# Camera ID for right camera
+CamR_id = 8# Camera ID for right camera
 
-#CamL= cv2.VideoCapture(CamL_id)
+CamR= cv2.VideoCapture(CamR_id, cv2.CAP_V4L2)
-#CamR= cv2.VideoCapture(CamR_id)
+CamL= cv2.VideoCapture(CamL_id, cv2.CAP_V4L2)
-
+'''
-
+while True:
-
+	ret, frame = CamR.read()
-
+	if ret:
-retL, imgL= cv2.VideoCapture(CamL_id, cv2.CAP_V4L2).read()
+		codec=CamR.get(cv2.CAP_PROP_FOURCC)
-retR, imgR= cv2.VideoCapture(CamR_id, cv2.CAP_V4L2).read()
+		print("codec: ",codec)
-print("les ret c'est",retL, retR)
+		sdfgh
-imgR_gray = cv2.cvtColor(imgR,cv2.COLOR_BGR2GRAY)
+'''
-imgL_gray = cv2.cvtColor(imgL,cv2.COLOR_BGR2GRAY)
-
-print("la c bon")
 # Reading the mapping values for stereo image rectification
 cv_file = cv2.FileStorage("data/params_py.xml", cv2.FILE_STORAGE_READ)
+
 Left_Stereo_Map_x = cv_file.getNode("Left_Stereo_Map_x").mat()
-#print(Left_Stereo_Map_x)
+
 Left_Stereo_Map_y = cv_file.getNode("Left_Stereo_Map_y").mat()
 Right_Stereo_Map_x = cv_file.getNode("Right_Stereo_Map_x").mat()
 Right_Stereo_Map_y = cv_file.getNode("Right_Stereo_Map_y").mat()
 cv_file.release()
 
+
 def nothing(x):
     pass
 
 cv2.namedWindow('disp',cv2.WINDOW_NORMAL)
-cv2.resizeWindow('disp',1000,800)
+cv2.resizeWindow('disp',600,600)
 
 cv2.createTrackbar('numDisparities','disp',1,17,nothing)
 cv2.createTrackbar('blockSize','disp',5,50,nothing)
 cv2.createTrackbar('preFilterType','disp',1,1,nothing)
 cv2.createTrackbar('preFilterSize','disp',2,25,nothing)
-
 cv2.createTrackbar('preFilterCap','disp',5,62,nothing)
-
 cv2.createTrackbar('textureThreshold','disp',10,100,nothing)
 cv2.createTrackbar('uniquenessRatio','disp',15,100,nothing)
-
 cv2.createTrackbar('speckleRange','disp',0,100,nothing)
 cv2.createTrackbar('speckleWindowSize','disp',3,25,nothing)
 cv2.createTrackbar('disp12MaxDiff','disp',5,25,nothing)
@@ -52,18 +48,19 @@ cv2.createTrackbar('minDisparity','disp',5,25,nothing)
 
 # Creating an object of StereoBM algorithm
 stereo = cv2.StereoBM_create()
+
 while True:
 	
 	# Capturing and storing left and right camera images
-	retL, imgL= cv2.VideoCapture(CamL_id, cv2.CAP_V4L2).read()
+	retL, imgL= CamL.read()
-	retR, imgR= cv2.VideoCapture(CamR_id, cv2.CAP_V4L2).read()
+	retR, imgR= CamR.read()
 	
+	waitkey(0)
 	# Proceed only if the frames have been captured
 	if retL and retR:
 		imgR_gray = cv2.cvtColor(imgR,cv2.COLOR_BGR2GRAY)
 		imgL_gray = cv2.cvtColor(imgL,cv2.COLOR_BGR2GRAY)
 
-
 		# Applying stereo image rectification on the left image
 		Left_nice= cv2.remap(imgL_gray,
 							Left_Stereo_Map_x,
@@ -83,13 +80,11 @@ while True:
 		# Updating the parameters based on the trackbar positions
 		numDisparities = cv2.getTrackbarPos('numDisparities','disp')*16
 		blockSize = cv2.getTrackbarPos('blockSize','disp')*2 + 5
-		
 		preFilterType = cv2.getTrackbarPos('preFilterType','disp')
 		preFilterSize = cv2.getTrackbarPos('preFilterSize','disp')*2 + 5
 		preFilterCap = cv2.getTrackbarPos('preFilterCap','disp')
 		textureThreshold = cv2.getTrackbarPos('textureThreshold','disp')
 		uniquenessRatio = cv2.getTrackbarPos('uniquenessRatio','disp')
-		
 		speckleRange = cv2.getTrackbarPos('speckleRange','disp')
 		speckleWindowSize = cv2.getTrackbarPos('speckleWindowSize','disp')*2
 		disp12MaxDiff = cv2.getTrackbarPos('disp12MaxDiff','disp')
@@ -98,13 +93,11 @@ while True:
 		# Setting the updated parameters before computing disparity map
 		stereo.setNumDisparities(numDisparities)
 		stereo.setBlockSize(blockSize)
-		
 		stereo.setPreFilterType(preFilterType)
 		stereo.setPreFilterSize(preFilterSize)
 		stereo.setPreFilterCap(preFilterCap)
 		stereo.setTextureThreshold(textureThreshold)
 		stereo.setUniquenessRatio(uniquenessRatio)
-		
 		stereo.setSpeckleRange(speckleRange)
 		stereo.setSpeckleWindowSize(speckleWindowSize)
 		stereo.setDisp12MaxDiff(disp12MaxDiff)
@@ -124,6 +117,7 @@ while True:
 
 		# Displaying the disparity map
 		cv2.imshow("disp",disparity)
+
 		# Close window using esc key
 		if cv2.waitKey(1) == 27:
 			break
@@ -131,24 +125,21 @@ while True:
 	else:
 		CamL= cv2.VideoCapture(CamL_id)
 		CamR= cv2.VideoCapture(CamR_id)
+	waitKey(0)
+print("Saving depth estimation paraeters ......")
 
-print("Saving depth estimation parameters ......")
+cv_file = cv2.FileStorage("../data/depth_estmation_params_py.xml", cv2.FILE_STORAGE_WRITE)
-
-cv_file = cv2.FileStorage("data/depth_estmation_params_py.xml", cv2.FILE_STORAGE_WRITE)
-cv_file.write("blockSize",blockSize)
-print ("aprés le write")
 cv_file.write("numDisparities",numDisparities)
-
+cv_file.write("blockSize",blockSize)
-
 cv_file.write("preFilterType",preFilterType)
 cv_file.write("preFilterSize",preFilterSize)
 cv_file.write("preFilterCap",preFilterCap)
 cv_file.write("textureThreshold",textureThreshold)
 cv_file.write("uniquenessRatio",uniquenessRatio)
-
 cv_file.write("speckleRange",speckleRange)
 cv_file.write("speckleWindowSize",speckleWindowSize)
 cv_file.write("disp12MaxDiff",disp12MaxDiff)
 cv_file.write("minDisparity",minDisparity)
 cv_file.write("M",39.075)
 cv_file.release()
+

file1.txt

@@ -1 +0,0 @@
-file1

file2.txt

@@ -1 +0,0 @@
-file2

stereocalib.py

@@ -0,0 +1,12 @@
+import cv2
+import numpy as np
+flags = 0
+flags |= cv2.CALIB_FIX_INTRINSIC
+# Here we fix the intrinsic camara matrixes so that only Rot, Trns, Emat and Fmat are calculated.
+# Hence intrinsic parameters are the same 
+ 
+criteria_stereo= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+ 
+ 
+# This step is performed to transformation between the two cameras and calculate Essential and Fundamenatl matrix
+retS, new_mtxL, distL, new_mtxR, distR, Rot, Trns, Emat, Fmat = cv2.stereoCalibrate(obj_pts, img_ptsL, img_ptsR, new_mtxL, distL, new_mtxR, distR, imgL_gray.shape[::-1], criteria_stereo, flags)

test12.py

@@ -1,28 +1,21 @@
 import cv2
 
-ID1 = 2
+ID1 = 6
-ID2 = 0
+ID2 = 8
-#cam1 = cv2.VideoCapture(ID2, cv2.CAP_V4L2)
+cam1 = cv2.VideoCapture(ID1, cv2.CAP_V4L2)
-#cam1.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
+cam1.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
-#cam2 = cv2.VideoCapture(ID1, cv2.CAP_V4L2)
+cam2 = cv2.VideoCapture(ID2, cv2.CAP_V4L2)
-#cam2.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
+cam2.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
-ret2, frame2 = cv2.VideoCapture(ID1, cv2.CAP_V4L2).read()
+
-ret1, frame1 = cv2.VideoCapture(ID2, cv2.CAP_V4L2).read()
+while True:
-while True:
+	ret1, frame1 = cam1.read()
+	ret2, frame2 = cam2.read()
 	
-	ret2, frame2 = cv2.VideoCapture(ID1, cv2.CAP_V4L2).read()
-	ret1, frame1 = cv2.VideoCapture(ID2, cv2.CAP_V4L2).read()
-	'''
-	print("ret 1 = ", ret1)
-	print("ret 2 = ", ret2)
-	'''
 	cv2.imshow('Camera 1', frame1)
 	cv2.imshow('Camera 2', frame2)
 	
-	
 	if cv2.waitKey(1) == ord('q'):
 		break
-
 cam1.release()
 cam2.release()
 cv2.destroyAllWindows()