Зміст курсу
Computer Vision Course Outline
Computer Vision Course Outline
Corner and Blob Detection
Corner Detection
Corner detection is used to identify sharp changes in intensity where two edges meet. It helps in feature matching, object tracking, and structure recognition.
Popular Methods:
- Harris Corner Detector (cv2.cornerHarris) - Detects corners based on gradient changes.
- Shi-Tomasi Corner Detector (cv2.goodFeaturesToTrack) - Selects the strongest corners in an image.
Blob Detection
Blob detection finds regions of similar intensity in an image, useful for object detection and tracking.
Popular Method:
- SimpleBlobDetector (cv2.SimpleBlobDetector) - Detects keypoints representing blobs based on size, shape, and intensity.
Swipe to start coding
Your task is to perform corner detection by Harris, Shi-Tomasi methods on a factory image and blob detection by Simple Blob Detector on a sunflower image with the following conditions:
- Harris Corner Detection:
- Block size:
2; - Kernel size:
3; - Harris detector free parameter (k):
0.04; - Mark detected corners in blue
[0, 0, 255].
- Block size:
- Shi-Tomasi Corner Detection:
- Maximum corners quantity:
100; - Quality level:
0.01; - Minimum distance between corners:
10; - Mark detected corners in green
[255, 255, 0].
- Maximum corners quantity:
- Blob Detection (
SimpleBlobDetector):- Maximal threshold:
127; - Filter by area:
- Minimal area:
100; - Maximal area:
500;
- Minimal area:
- Filter by circularity:
- Minimal circularity:
0.02(detects irregular shapes);
- Minimal circularity:
- Filter by convexity:
- Minimal convexity:
0.5(allows concave blobs);
- Minimal convexity:
- Filter by inertia:
- Minimal inertia ratio:
0.001(detects elongated blobs);
- Minimal inertia ratio:
- Mark detected blobs in green
[255, 0, 255]usingcv2.drawKeypoints()withcv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTSflag.
- Maximal threshold:
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Corner and Blob Detection
Corner Detection
Corner detection is used to identify sharp changes in intensity where two edges meet. It helps in feature matching, object tracking, and structure recognition.
Popular Methods:
- Harris Corner Detector (cv2.cornerHarris) - Detects corners based on gradient changes.
- Shi-Tomasi Corner Detector (cv2.goodFeaturesToTrack) - Selects the strongest corners in an image.
Blob Detection
Blob detection finds regions of similar intensity in an image, useful for object detection and tracking.
Popular Method:
- SimpleBlobDetector (cv2.SimpleBlobDetector) - Detects keypoints representing blobs based on size, shape, and intensity.
Swipe to start coding
Your task is to perform corner detection by Harris, Shi-Tomasi methods on a factory image and blob detection by Simple Blob Detector on a sunflower image with the following conditions:
- Harris Corner Detection:
- Block size:
2; - Kernel size:
3; - Harris detector free parameter (k):
0.04; - Mark detected corners in blue
[0, 0, 255].
- Block size:
- Shi-Tomasi Corner Detection:
- Maximum corners quantity:
100; - Quality level:
0.01; - Minimum distance between corners:
10; - Mark detected corners in green
[255, 255, 0].
- Maximum corners quantity:
- Blob Detection (
SimpleBlobDetector):- Maximal threshold:
127; - Filter by area:
- Minimal area:
100; - Maximal area:
500;
- Minimal area:
- Filter by circularity:
- Minimal circularity:
0.02(detects irregular shapes);
- Minimal circularity:
- Filter by convexity:
- Minimal convexity:
0.5(allows concave blobs);
- Minimal convexity:
- Filter by inertia:
- Minimal inertia ratio:
0.001(detects elongated blobs);
- Minimal inertia ratio:
- Mark detected blobs in green
[255, 0, 255]usingcv2.drawKeypoints()withcv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTSflag.
- Maximal threshold:
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів