[cv2 cheatsheet] #cv2

find_corners.py

# number of inner corners in x and y direction
nx = 8
ny = 6

#find chessboard corners
img = mpimg.imread(images[idx])

# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)

# If found, draw corners
if ret == True:
    # Draw and display the corners
    cv2.drawChessboardCorners(img, (nx, ny), corners, ret)
    plt.imshow(img)

intrinsic_calibration.py

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.

# Make a list of calibration images
images = glob.glob('./Cal*.jpg')

# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = mpimg.imread(fname)
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)

    # If found, add object points, image points
    if ret == True:
        objpoints.append(objp)
        imgpoints.append(corners)

# compute the calibration coefficient
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)

# undistort the image
img = # read from somewhere
undist = cv2.undistort(img, mtx, dist, None, mtx)


#visualize and compare
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undist)
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

transform_to_hsv.py

hsv_img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)