Please help me in this python project...

1. Use Harris corner detection technique to find the key points of the enclosed image. You may use the built-in function/library (cv2.goodFeaturesToTrack()) or any other one for this part. Display the original image with all detected points on the image. Display the original image with the strongest 300 points on the image. 2. Write a program to perform K-means clustering technique using the 300 points of part 1. You need to create your own function/code for this part (K-mean algorithm). Compare the outcomes of multiple runs with different K and choose the best one based on a predefined criterion. In general, a large K probably decreases the error but increases the risk of overfitting. Display the original image with the best detected clusters on it (use different colors). 3. Write a program to draw a bounding box for each cluster of the data points of part 2. Display the original image with the bounding boxes on it (use different colors). Notes: The project should be implemented in Python. . The project report should be submitted through Blackboard for evaluation, which includes program outline with flowchart and equations used, implementation results with sample data sets and the obtained results. The program codes along with the dataset used for testing and validation should be submitted through Blackboard for evaluation. 1. Use Harris corner detection technique to find the key points of the enclosed image. You may use the built-in function/library (cv2.goodFeaturesToTrack()) or any other one for this part. Display the original image with all detected points on the image. Display the original image with the strongest 300 points on the image. 2. Write a program to perform K-means clustering technique using the 300 points of part 1. You need to create your own function/code for this part (K-mean algorithm). Compare the outcomes of multiple runs with different K and choose the best one based on a predefined criterion. In general, a large K probably decreases the error but increases the risk of overfitting. Display the original image with the best detected clusters on it (use different colors). 3. Write a program to draw a bounding box for each cluster of the data points of part 2. Display the original image with the bounding boxes on it (use different colors). Notes: The project should be implemented in Python. . The project report should be submitted through Blackboard for evaluation, which includes program outline with flowchart and equations used, implementation results with sample data sets and the obtained results. The program codes along with the dataset used for testing and validation should be submitted through Blackboard for evaluation