Problem 1: Harris Corner Detection Complete the python function to implement the Harris corner detection algorithm. The function should take an input image, apply the necessary preprocessing steps, perform corner detection using the Harris algorithm, and display the original image with the detected corners marked. Implement the Harris corner detection algorithm from scratch using Python and NumPy. Load the input image using any suitable library (e.g., OpenCV, PIL). Step 1. Perform the following preprocessing steps on the input image: Step 1.1 Convert the image to grayscale. Step 1.2 Apply Gaussian blur with a suitable kernel size. Step 2. Implement the Harris corner detection algorithm: Step 2.1 Calculate the gradients of the image using Sobel filters. Step 2.2 Compute the structure tensor and the Harris response function. Step 2.3. Apply non-maximum suppression to find the corner points. Step 2.4. Mark the corner points on the original image. def harris_corner_detection(image, kernel_size, k, threshold): # Convert image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Apply Gaussian blur blurred = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0) # Calculate gradients using Sobel filters gradient_x = cv2.Sobel(blurred, cv2.CV_64F, 1, 0) gradient_y = cv2.Sobel(blurred, cv2.CV_64F, 0, 1) # Compute the structure tensor elements gradient_xx = gradient_xy = gradient_yy = # Apply Gaussian filter to structure tensor elements gradient_xx = misc.gaussian_filter(gradient_x**2, sigma = 1) gradient_xy = misc.gaussian_filter(gradient_y*gradient_x, sigma = 1) gradient_yy = misc.gaussian_filter(gradient_y**2, sigma = 1) # Compute the Harris response function det = gradient_xx * gradient_yy - gradient_xy ** 2 trace = gradient_xx + gradient_xy harris_response = det - k * trace ** 2 # Apply non-maximum suppression to find corner points corner_mask = np.zeros_like(image) ##########--WRITE YOUR CODE HERE--########## # Mark corners in red for row, i in enumerate(harris_response): for col, j in enumerate(i): if j > 0: image[row, col] = [255, 0, 0] ##########-------END OF CODE-------########## # Overlay corner points on the original image output_image = image return output_image

Problem 1: Harris Corner Detection Complete the python function to implement the Harris corner detection algorithm. The function should take an input image, apply the necessary preprocessing steps, perform corner detection using the Harris algorithm, and display the original image with the detected corners marked. Implement the Harris corner detection algorithm from scratch using Python and NumPy. Load the input image using any suitable library (e.g., OpenCV, PIL). Step 1. Perform the following preprocessing steps on the input image: Step 1.1 Convert the image to grayscale. Step 1.2 Apply Gaussian blur with a suitable kernel size. Step 2. Implement the Harris corner detection algorithm: Step 2.1 Calculate the gradients of the image using Sobel filters. Step 2.2 Compute the structure tensor and the Harris response function. Step 2.3. Apply non-maximum suppression to find the corner points. Step 2.4. Mark the corner points on the original image. def harris_corner_detection(image, kernel_size, k, threshold): # Convert image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Apply Gaussian blur blurred = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0) # Calculate gradients using Sobel filters gradient_x = cv2.Sobel(blurred, cv2.CV_64F, 1, 0) gradient_y = cv2.Sobel(blurred, cv2.CV_64F, 0, 1) # Compute the structure tensor elements gradient_xx = gradient_xy = gradient_yy = # Apply Gaussian filter to structure tensor elements gradient_xx = misc.gaussian_filter(gradient_x**2, sigma = 1) gradient_xy = misc.gaussian_filter(gradient_y*gradient_x, sigma = 1) gradient_yy = misc.gaussian_filter(gradient_y**2, sigma = 1) # Compute the Harris response function det = gradient_xx * gradient_yy - gradient_xy ** 2 trace = gradient_xx + gradient_xy harris_response = det - k * trace ** 2 # Apply non-maximum suppression to find corner points corner_mask = np.zeros_like(image) ##########--WRITE YOUR CODE HERE--########## # Mark corners in red for row, i in enumerate(harris_response): for col, j in enumerate(i): if j > 0: image[row, col] = [255, 0, 0] ##########-------END OF CODE-------########## # Overlay corner points on the original image output_image = image return output_image

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Problem 1: Harris Corner Detection

Complete the python function to implement the Harris corner detection algorithm. The function should take an input image, apply the necessary preprocessing steps, perform corner detection using the Harris algorithm, and display the original image with the detected corners marked. Implement the Harris corner detection algorithm from scratch using Python and NumPy. Load the input image using any suitable library (e.g., OpenCV, PIL).

Step 1. Perform the following preprocessing steps on the input image:
- Step 1.1 Convert the image to grayscale.
- Step 1.2 Apply Gaussian blur with a suitable kernel size.
Step 2. Implement the Harris corner detection algorithm:
- Step 2.1 Calculate the gradients of the image using Sobel filters.
- Step 2.2 Compute the structure tensor and the Harris response function.
- Step 2.3. Apply non-maximum suppression to find the corner points.
- Step 2.4. Mark the corner points on the original image.

def harris_corner_detection(image, kernel_size, k, threshold):

# Convert image to grayscale

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply Gaussian blur

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

# Calculate gradients using Sobel filters

gradient_x = cv2.Sobel(blurred, cv2.CV_64F, 1, 0)

gradient_y = cv2.Sobel(blurred, cv2.CV_64F, 0, 1)

# Compute the structure tensor elements

gradient_xx =

gradient_xy =

gradient_yy =

# Apply Gaussian filter to structure tensor elements

gradient_xx = misc.gaussian_filter(gradient_x**2, sigma = 1)

gradient_xy = misc.gaussian_filter(gradient_y*gradient_x, sigma = 1)

gradient_yy = misc.gaussian_filter(gradient_y**2, sigma = 1)

# Compute the Harris response function

det = gradient_xx * gradient_yy - gradient_xy ** 2

trace = gradient_xx + gradient_xy

harris_response = det - k * trace ** 2

# Apply non-maximum suppression to find corner points

corner_mask = np.zeros_like(image)

##########--WRITE YOUR CODE HERE--##########

# Mark corners in red

for row, i in enumerate(harris_response):

for col, j in enumerate(i):

if j > 0:

image[row, col] = [255, 0, 0]

##########-------END OF CODE-------##########

# Overlay corner points on the original image

output_image = image

return output_image

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