or run

tessl search
Log in

Version

Workspace
tessl
Visibility
Public
Created
Last updated
Describes
pypipkg:pypi/imutils@0.5.x

evals

scenario-9

rubric.jsontask.md
tile.json

tessl/pypi-imutils

tessl install tessl/pypi-imutils@0.5.0

A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib images, sorting contours, detecting edges, and much more easier with OpenCV and both Python 2.7 and Python 3.

Agent Success

Agent success rate when using this tile

91%

Improvement

Agent success rate improvement when using this tile compared to baseline

1.34x

Baseline

Agent success rate without this tile

68%

task.mdevals/scenario-9/

Image Feature Matcher

A tool that extracts and matches distinctive keypoint features between two images to find correspondences.

Capabilities

Feature Detection and Description

  • Detects keypoints in the first image and computes enhanced descriptors @test
  • Detects keypoints in the second image and computes enhanced descriptors @test

Feature Matching

  • Matches descriptors between two images and returns good matches @test
  • Returns an empty list when no good matches are found between images @test

Visualization

  • Draws matches between two images with connecting lines @test

Implementation

@generates

API

def detect_and_compute(image):
    """
    Detects keypoints in the image and computes enhanced descriptors.

    Args:
        image: Input image as a NumPy array (grayscale or color)

    Returns:
        tuple: (keypoints, descriptors) where keypoints is a list of cv2.KeyPoint
               objects and descriptors is a NumPy array of feature descriptors
    """
    pass

def match_features(kp1, desc1, kp2, desc2, ratio=0.75):
    """
    Matches features between two sets of keypoints and descriptors.

    Args:
        kp1: List of keypoints from first image
        desc1: Descriptors from first image
        kp2: List of keypoints from second image
        desc2: Descriptors from second image
        ratio: Ratio test threshold for filtering good matches (default 0.75)

    Returns:
        list: Good matches as a list of cv2.DMatch objects
    """
    pass

def visualize_matches(img1, kp1, img2, kp2, matches):
    """
    Creates a visualization showing matched keypoints between two images.

    Args:
        img1: First image
        kp1: Keypoints from first image
        img2: Second image
        kp2: Keypoints from second image
        matches: List of good matches

    Returns:
        numpy.ndarray: Image showing the matches with connecting lines
    """
    pass

Dependencies { .dependencies }

imutils { .dependency }

Provides enhanced feature detection and description capabilities for computer vision tasks.

opencv-python { .dependency }

Provides core computer vision operations including image processing and feature detection.

numpy { .dependency }

Provides array operations for numerical computations.