session240523

calib.py

@@ -1,85 +0,0 @@
-
-#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

@@ -1,54 +0,0 @@
-# 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((8*5,3), np.float32)
+objp = np.zeros((21*15,3), np.float32)
-objp[:,:2] = np.mgrid[0:8,0:5].T.reshape(-1,2)
+objp[:,:2] = np.mgrid[0:21,0:15].T.reshape(-1,2)
 
 img_ptsL = []
 img_ptsR = []
 obj_pts = []
 
-for i in tqdm(range(1,5)):
+for i in tqdm(range(1,6)): #number of images
 	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)
@@ -30,16 +30,22 @@ for i in tqdm(range(1,5)):
 	outputL = imgL.copy()
 	outputR = imgR.copy()
 
-	retR, cornersR =  cv2.findChessboardCorners(outputR,(8,5),None)
+
-	retL, cornersL = cv2.findChessboardCorners(outputL,(8,5),None)
+	retR, cornersR =  cv2.findChessboardCorners(outputR,(21,15),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,(8,5),cornersR,retR)
+		cv2.drawChessboardCorners(outputR,(21,15),cornersR,retR)
-		cv2.drawChessboardCorners(outputL,(8,5),cornersL,retL)
+		cv2.drawChessboardCorners(outputL,(21,15),cornersL,retL)
 		cv2.imshow('cornersR',outputR)
 		cv2.imshow('cornersL',outputL)
 		cv2.waitKey(0)

disparityParam_gui.py

@@ -4,8 +4,8 @@ import cv2
 
 # Check for left and right camera IDs
 # These values can change depending on the system
-CamL_id = 2# Camera ID for left camera
+CamL_id = 0# Camera ID for left camera
-CamR_id = 0# Camera ID for right camera
+CamR_id = 2# Camera ID for right camera
 
 #CamL= cv2.VideoCapture(CamL_id)
 #CamR= cv2.VideoCapture(CamR_id)
@@ -15,7 +15,7 @@ CamR_id = 0# Camera ID for right camera
 
 retL, imgL= cv2.VideoCapture(CamL_id, cv2.CAP_V4L2).read()
 retR, imgR= cv2.VideoCapture(CamR_id, cv2.CAP_V4L2).read()
-
+print("les ret c'est",retL, retR)
 imgR_gray = cv2.cvtColor(imgR,cv2.COLOR_BGR2GRAY)
 imgL_gray = cv2.cvtColor(imgL,cv2.COLOR_BGR2GRAY)
 
@@ -23,7 +23,7 @@ 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)
+#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()
@@ -132,7 +132,7 @@ while True:
 		CamL= cv2.VideoCapture(CamL_id)
 		CamR= cv2.VideoCapture(CamR_id)
 
-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.write("blockSize",blockSize)

stereocalib.py

@@ -1,12 +0,0 @@
-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)