Introduction to Image Processing, Computer Vision and Deep Learning

目錄

• 1. Find Contour
  • 1) Draw Contour
    • Follow the steps:
    • Example code:
  • 2) Count Coins
• 2. Camera Calibration
  • Relationship of 2D x Intrinsic x Extrinsic x 3D
  • 1) Corner detection
    • Follow the steps:
    • Example code:
  • 2) Find the intrinsic matrix
    • Follow the steps:
    • Example code:
  • 3) Find the extrinsic matrix
    • Follow the steps:
    • Example code:
  • 4) Find the distortion matrix
    • Follow the steps:
    • Example code:
• 3. Augmented Reality
  • • Follow the steps:
    • Example code:
• 4. Stereo Disparity Map
  • 1) Compute disparity map
    • Follow the steps:
  • 2) Calculate the depth
    • Follow the steps:
    • Example code:

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1. Find Contour

1) Draw Contour

Follow the steps:

1. RGB -> Grayscale -> Binary

# Grayscale
grayImage = cv2.cvtColor(originalImage, cv2.COLOR_BGR2GRAY)
# Binary
(thresh, blackAndWhiteImage) = cv2.threshold(grayImage, 127, 255, cv2.THRESH_BINARY)

2. Remember to use Gaussian Blur to remove the noise.

blur = cv2.GaussianBlur(gray,(kernel_size, kernel_size), 0)

3. Using some edge detection functions to get better results. (Ex: cv2.Canny)

edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

4. use cv2.findContours

image, countours, hierarchy = cv2.findContours(image, mode, method[, contours[, hierarchy[, offset ]]])  

5. use cv2.drawContours

cv2.drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset ]]]]]) 

Example code:

image = cv2.imread('./Datasets/Q1_Image/coin01.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        (thresh, binary) = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)
guassian = cv2.GaussianBlur(binary, (11, 11), 0)
edge_image = cv2.Canny(guassian, 127, 127)

edge_image, contours, hierarchy = cv2.findContours(edge_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
image_copy = image.copy()
cv2.drawContours(image_copy, contours, -1, (0, 0, 255), 2)
        # image_copy[edge_image > 0.01 * edge_image.max()] = [0, 0, 255]

cv2.namedWindow('coin01')
cv2.imshow('coin01', image_copy)

2) Count Coins

count = len(contours)

2. Camera Calibration

Relationship of 2D x Intrinsic x Extrinsic x 3D

1) Corner detection

Follow the steps:

1. Given 15 of chessboard images
2. Read the images and convert to grayscale

# Grayscale
grayImage = cv2.cvtColor(chess_board_image, cv2.COLOR_BGR2GRAY)

3. Find the chessboard corners

#Enter the number of inside corners in (x, y) = (nx, ny)
ret, corners = cv2.findChessboardCorners(grayImage, (nx, ny), None)

4. Draw and display the corners

cv2.drawChessboardCorners(chess_board_image, (nx, ny), corners, ret)

Example code:

chess_images = glob.glob('./Datasets/Q2_Image/*.bmp')
# Select any index to grab an image from the list
for i in range(len(chess_images)):
    # Read in the image
    chess_board_image = cv2.imread(chess_images[i])
    # Convert to grayscale
    gray = cv2.cvtColor(chess_board_image, cv2.COLOR_BGR2GRAY)
    # Find the chessboard corners
    ny = 8
    nx = 11
    ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
    # If found, draw corners
    if ret == True:
        # Draw and display the corners
        cv2.drawChessboardCorners(chess_board_image, (nx, ny), corners, ret)
        result_name = 'board' + str(i+1) + '.bmp'
        cv2.imwrite(result_name, chess_board_image)
        cv2.namedWindow("%s" % (i+1))
        cv2.imshow("%s" % (i+1), chess_board_image)

cv2.waitKey(0)
cv2.destroyAllWindows()

2) Find the intrinsic matrix

Find the intrinsic matrix:

Follow the steps:

1. termination criteria

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

2. prepare object points, like (0,0,0), (1,0,0), (2,0,0) …,(6,5,0)

objp = np.zeros((11 * 8, 3), np.float32)
objp[:, :2] = np.mgrid[0:8, 0:11].T.reshape(-1, 2)

3. Arrays to store object points and image points from all the images

objpoints = []  # 3d point in real world space
imgpoints = []  # 2d points in image plane

4. Select all images, convert to grayscale, and find the chessboard corners

for i in range(len(chess_images)):
    # Read in the image
    image = cv2.imread(chess_images[i])
    # Convert to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (8, 11), None)

5. Further optimization calculations on the corners

corners2 = cv2.cornerSubPix(gray, corners, (7, 7), (-1, -1), criteria)

6. use cv2.calibrateCamera

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

Example code:

# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((11 * 8, 3), np.float32)
objp[:, :2] = np.mgrid[0:8, 0:11].T.reshape(-1, 2)

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

chess_images = glob.glob('./Datasets/Q2_Image/*.bmp')
# Select any index to grab an image from the list
for i in range(len(chess_images)):
    # Read in the image
    image = cv2.imread(chess_images[i])
    # Convert to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (8, 11), None)

    if ret == True:
        objpoints.append(objp)

        corners2 = cv2.cornerSubPix(gray, corners, (7, 7), (-1, -1), criteria)
        imgpoints.append(corners2)

# gray.shape[::-1] = (2048, 2048)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (2048, 2048), None, None)
print(mtx)

3) Find the extrinsic matrix

Extrinsic matrix:

Follow the steps:

1. Follow 2) Find the intrinsic matrix
2. use cv2.Rodrigues 做旋轉向量和旋轉矩陣的轉換
3. use np.hstack 做陣列橫向合併

Bonus: 陣列縱向合併–np.vstack()

Example code:

# gray.shape[::-1] = (2048, 2048)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (2048, 2048), None, None)
R = cv2.Rodrigues(rvecs[num-1])
ext = np.hstack((R[0], tvecs[num-1]))
print(ext)

4) Find the distortion matrix

Distortion Coefficients: k1, k2, k3, p1, p2

Follow the steps:

1. Follow 2) Find the intrinsic matrix

Example code:

# gray.shape[::-1] = (2048, 2048)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (2048, 2048), None, None)
print(dist)

參考：

1. https://docs.opencv.org/3.4/d9/d0c/group__calib3d.html#ga687a1ab946686f0d85ae0363b5af1d7b

3. Augmented Reality

Follow the steps:

1. Follow 2) Find the intrinsic matrix
2. prepare object points which you want to draw, like (3,3,-3), (1,1,0), (3,5,0), (5,1,0)
3. use cv2.projectPoints to project 3D points to image plane

imgpts, jac = cv2.projectPoints(axis, rvecs[i], tvecs[i], mtx, dist)

4. use cv2.line draw line

Example code:

# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((11 * 8, 3), np.float32)
objp[:, :2] = np.mgrid[0:8, 0:11].T.reshape(-1, 2)

# axis = np.float32([[3, 3, -3], [1, 1, 0], [3, 5, 0], [5, 1, 0]]).reshape(-1, 3)
axis = np.float32([[5, 3, -3], [7, 1, 0], [3, 3, 0], [7, 5, 0]]).reshape(-1, 3)

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

chess_images = glob.glob('./Datasets/Q3_Image/*.bmp')
# Select any index to grab an image from the list
for i in range(len(chess_images)):
    # Read in the image
    image = cv2.imread(chess_images[i])
    # Convert to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (8, 11), None)

    if ret == True:
        objpoints.append(objp)
        # objp = 8 * 11 objpoints (x, y, z)
        corners2 = cv2.cornerSubPix(gray, corners, (7, 7), (-1, -1), criteria)
        imgpoints.append(corners2)
        # corner2 = each object point on 2D image (x, y)

        # gray.shape[::-1] = (2048, 2048)
        ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (2048, 2048), None, None)

        # project 3D points to image plane
        imgpts, jac = cv2.projectPoints(axis, rvecs[i], tvecs[i], mtx, dist)

        def draw(image, imgpts):
            image = cv2.line(image, tuple(imgpts[0].ravel()), tuple(imgpts[1].ravel()), (0, 0, 255), 5)
            image = cv2.line(image, tuple(imgpts[0].ravel()), tuple(imgpts[2].ravel()), (0, 0, 255), 5)
            image = cv2.line(image, tuple(imgpts[0].ravel()), tuple(imgpts[3].ravel()), (0, 0, 255), 5)
            image = cv2.line(image, tuple(imgpts[1].ravel()), tuple(imgpts[2].ravel()), (0, 0, 255), 5)
            image = cv2.line(image, tuple(imgpts[1].ravel()), tuple(imgpts[3].ravel()), (0, 0, 255), 5)
            image = cv2.line(image, tuple(imgpts[2].ravel()), tuple(imgpts[3].ravel()), (0, 0, 255), 5)
            return image

        img = draw(image, imgpts)

        cv2.imwrite('%s_v.jpg' % i, img)
        img = cv2.resize(img, (1024, 1024), interpolation=cv2.INTER_AREA)
        cv2.namedWindow('img')
        cv2.imshow('img', img)
        cv2.waitKey(5)

參考：

1. https://docs.opencv.org/3.4/d9/d0c/group__calib3d.html#ga1019495a2c8d1743ed5cc23fa0daff8c
2. https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_calib3d/py_pose/py_pose.html

4. Stereo Disparity Map

Original image(left&right):

Stereo Disparity map:

1) Compute disparity map

Follow the steps:

1. open image left and right
2. use cv2.StereoBM_create, stereo.compute

2) Calculate the depth

這題做的時候有問題，disparity[y][x]的值一直是0，真的值output不出來QQ
所以code的部分參考參考，可以修改更好。

Follow the steps:

1. focal_len = 2826
   baseline = 178
   Cx(𝑐_𝑥^𝑟𝑖𝑔ℎ𝑡 − 𝑐_𝑥^𝑙𝑒𝑓𝑡) = 123
   We know that:
   d(distance) = (your point) - Cx
   Z(depth) = focal_length * baseline / d
2. use cv2.setMouseCallback to show the points which you clicked
3. use cv2.rectangle to draw the point
4. use cv2.putText to show disparity and depth

Example code:

imgL = cv2.imread('./Datasets/Q4_Image/imgL.png', 0)
imgR = cv2.imread('./Datasets/Q4_Image/imgR.png', 0)

stereo = cv2.StereoBM_create(numDisparities=256, blockSize=25)
disparity = stereo.compute(imgL, imgR).astype(np.float32) / 16.0

disparity = cv2.resize(disparity, (1400, 950), interpolation=cv2.INTER_AREA)
# cv2.namedWindow('disparity')
# cv2.imshow('disparity', disparity)
# cv2.imwrite('disparity.jpg', disparity)

focal_len = 2826
baseline = 178
Cx = 123

# mouse callback function
def draw_circle(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        # print(x, ",", y)
        cv2.rectangle(disparity, (x-3, y-3), (x+3, y+3), (0, 0, 255), -1)
        dist = disparity[y][x] - Cx
        depth = int(focal_len * baseline / abs(dist))
        # print("Disparity: " + str(disparity[x][y]) + " pixels")
        # print("Depth: " + str(depth) + " mm")

        # text = disparity.copy()
        cv2.rectangle(disparity, (1100, 850), (1390, 940), (255, 255, 255), -1)
        cv2.putText(disparity, "Disparity: " + str(int(disparity[y][x])) + " pixels", (1120, 890),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2, cv2.LINE_AA)
        cv2.putText(disparity, "Depth: " + str(depth) + " mm", (1120, 930),
                            cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 0, 0), 2, cv2.LINE_AA)
        # image = np.hstack([disparity, text])
        cv2.imshow('image', disparity)

while(1):
    cv2.namedWindow('image')
    cv2.setMouseCallback('image', draw_circle)
    cv2.imshow('image', disparity)
    if cv2.waitKey(20) & 0xFF == 27:
        break

cv2.waitKey(0)
cv2.destroyAllWindows()