aculisme / microcv

Petri dish OpenCV blob detection

MicroCV

Petri dish OpenCV feature detection

Installation

Assuming Python 3 and Pip are installed, run python -m pip install -r requirements.txt to set up the necessary requirements. Replace python with your own python installation alias if needed.

Usage

From the command line, run python main.py IMAGE_FILENAME, where IMAGE_FILENAME is the petri dish image to analyze (e.g. example1.png).

Quit the Python window by pressing q, or quitting the process from the command line.

Save a snapshot of the analyzed image (with circles overlaid) by pressing s. This image will be saved in the saved_images folder.

Interface

The provided sliders may be used to refine the feature detection parameters. The sliders correspond to the following parameters:

• min_dist: Minimum distance between detected centers.
• edge_threshold: Upper threshold for the internal Canny edge detector.
• centre_threshold: Threshold for center detection.
• min_radius: Minimum radius to be detected. If unknown, put zero as default.
• max_radius: Maximum radius to be detected. If unknown, put zero as default.

Note that even with optimal settings, some features may not be detected. MicroCV is a tool intended to aid microbial growth enumeration -- not automate it entirely. It is your responsibility to manually add false negatives / remove false positives from your tally.

Background info

The algorithm behind MicroCV works in three steps:

• first, it pre-processes the provided image by gray-scaling and blurring it
• next, it applies Circle Hough Transforms to the image with parameters defined by the interface sliders, extracting the locations of all detected growths
• lastly, circles are drawn at the extracted location and radius.

While binary mask -based algorithms are generally preferred for petri dish analysis, this CHT-based algorithm performs better given petri dish images with complex backgrounds, as was specified here.