Pixel Color Count¶

Count pixels in an image that match a target color within a specified tolerance range, using color range masking to identify matching pixels and returning the total count of pixels within the color tolerance range for color analysis, quality control, color-based measurements, and pixel-level color quantification workflows.

How This Block Works¶

This block counts how many pixels in an image match a specific target color within a tolerance range, providing pixel-level color quantification. The block:

1. Receives an input image and a target color specification (hex string, RGB tuple string, or RGB tuple)
2. Converts the target color to BGR (Blue-Green-Red) format for OpenCV processing:
3. Parses hex color strings (e.g., "#431112" or "#412" shorthand)
4. Parses RGB tuple strings (e.g., "(128, 32, 64)")
5. Handles RGB tuples directly (e.g., (18, 17, 67))
6. Converts RGB to BGR format (reverses color channel order) since OpenCV uses BGR
7. Calculates color tolerance bounds:
8. Creates a lower bound by subtracting tolerance from each BGR channel of the target color
9. Creates an upper bound by adding tolerance to each BGR channel of the target color
10. Clips bounds to valid 0-255 range for each channel
11. Defines a 3D color cube in BGR space where matching pixels must fall
12. Creates a binary mask using OpenCV's inRange function:
13. Compares each pixel's BGR values against the lower and upper bounds
14. Sets mask pixel to 255 (white) if pixel color falls within the tolerance range
15. Sets mask pixel to 0 (black) if pixel color falls outside the tolerance range
16. Uses vectorized operations for efficient pixel-level comparison across the entire image
17. Counts matching pixels:
18. Counts non-zero pixels in the mask (pixels with value 255, representing matches)
19. Returns the total count of pixels that match the target color within tolerance

The block performs pixel-level color matching using a tolerance-based approach, allowing for slight color variations due to compression, lighting, or image processing. The tolerance creates a range around the target color - a tolerance of 10 means pixels can differ by up to ±10 in each BGR channel (for a total range of 21 values per channel). Lower tolerance values (e.g., 5-10) require very close color matches, while higher tolerance values (e.g., 20-30) allow more color variation. This is useful for counting pixels of a specific color when exact matches may not exist due to image artifacts or processing.

Common Use Cases¶

• Color Area Measurement: Measure the area or coverage of specific colors in images (e.g., measure coverage of specific colors in images, quantify color distribution, assess color proportions), enabling color area quantification workflows
• Quality Control and Inspection: Count pixels of expected colors for quality control (e.g., verify color consistency in products, detect color defects, validate expected colors in images), enabling color-based quality control workflows
• Color-Based Analysis: Analyze images based on specific color presence or quantity (e.g., analyze color distribution in images, quantify color usage, measure color characteristics), enabling color quantification analysis workflows
• Image Processing Validation: Validate image processing results by counting expected colors (e.g., verify color transformations, validate color corrections, check color filtering results), enabling color validation workflows
• Feature Detection and Measurement: Detect and measure features based on color characteristics (e.g., count pixels in colored regions, measure color-based features, quantify color-defined areas), enabling color-based feature measurement workflows
• Threshold-Based Color Detection: Use pixel counting for threshold-based color detection (e.g., detect if enough pixels match a color, determine color presence thresholds, implement color-based triggers), enabling threshold-based color detection workflows

Connecting to Other Blocks¶

This block receives an image and target color, and produces a pixel count:

• After image input blocks to count pixels of specific colors in input images (e.g., count color pixels in camera feeds, analyze colors in image inputs, quantify colors in images), enabling color pixel counting workflows
• After crop blocks to count pixels in specific image regions (e.g., count color pixels in cropped regions, analyze colors in specific areas, quantify colors in selected regions), enabling region-based color pixel counting
• After preprocessing blocks to count pixels after image processing (e.g., count colors after filtering, analyze colors after enhancement, quantify colors after transformations), enabling processed image color counting workflows
• Before filtering or logic blocks that use pixel counts for decision-making (e.g., filter based on pixel counts, make decisions based on color quantities, apply logic based on pixel counts), enabling count-based conditional workflows
• Before data storage blocks to store pixel count information (e.g., store color pixel counts with images, save color analysis results, record color quantification data), enabling color count metadata storage workflows
• In quality control workflows where pixel counting validates color characteristics (e.g., verify color quantities in quality control, validate color coverage, check color consistency), enabling color-based quality control workflows

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/pixel_color_count@v1to add the block as as step in your workflow.

Properties¶

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Pixel Color Count in version v1 has.

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/pixel_color_count@v1",
    "image": "$inputs.image",
    "target_color": "#431112",
    "tolerance": 10
}