cv-dot-annotation-blob-diff-extraction

Overview

Many wildlife/cell/object-counting datasets ship annotations as a second copy of the image with colored dots painted over each instance. You have to recover the point list yourself. The canonical recipe is four steps: cv2.absdiff(dotted, raw) to isolate the dots, bitwise-mask out any blacked-out exclusion regions present in either image, run skimage.feature.blob_log with tight sigma bounds matched to the known dot radius, then classify each blob by reading the centroid pixel color from the dotted image (not the diff — the diff desaturates the color). Used in NOAA Steller Sea Lion Population Count top kernels.

Quick Start

import cv2, numpy as np, skimage.feature

img_raw = cv2.imread(raw_path)
img_dot = cv2.imread(dotted_path)
diff = cv2.absdiff(img_dot, img_raw)

# mask out blacked-out regions (annotator exclusions) from either image
m1 = cv2.cvtColor(img_dot, cv2.COLOR_BGR2GRAY); m1[m1 < 20] = 0; m1[m1 > 0] = 255
m2 = cv2.cvtColor(img_raw, cv2.COLOR_BGR2GRAY); m2[m2 < 20] = 0; m2[m2 > 0] = 255
diff = cv2.bitwise_or(diff, diff, mask=m1)
diff = cv2.bitwise_or(diff, diff, mask=m2)

gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
blobs = skimage.feature.blob_log(gray, min_sigma=3, max_sigma=4, num_sigma=1, threshold=0.02)

points = []
for y, x, _ in blobs:
    b, g, r = img_dot[int(y), int(x)]
    if   r > 200 and b < 50  and g < 50:              cls = 'adult_male'
    elif r > 200 and b > 200 and g < 50:              cls = 'subadult_male'
    elif r < 100 and g > 100 and b < 100:             cls = 'juvenile'
    elif r < 100 and g < 100 and 150 < b < 200:       cls = 'pup'
    elif r < 150 and g < 50  and b < 100:             cls = 'adult_female'
    else:                                              continue
    points.append((int(x), int(y), cls))

Workflow

1. Compute cv2.absdiff(dotted, raw) to leave only the dot pixels
2. Build blackout masks from both images by thresholding grayscale < 20 and bitwise-OR the diff through them
3. Convert to grayscale and run blob_log with min/max_sigma bracketing the expected dot radius and num_sigma=1 for speed
4. For each blob centroid, sample the BGR pixel from the original dotted image (preserves saturation)
5. Route the pixel through a hand-tuned RGB decision tree; drop fall-throughs to an error bucket to quantify noise

Key Decisions

• absdiff, not signed subtract: color-agnostic and symmetric across image order.
• Mask from BOTH images: annotators black out regions in either copy; missing either mask leaks false blobs.
• num_sigma=1 with tight sigma range: single-scale LoG is fast and precise when dot radius is fixed.
• Sample color from img_dot, not diff: the diff fades saturated colors, wrecking the decision tree.
• Explicit error fallback: catches out-of-gamut dots and quantifies labeling noise rather than silently mislabeling.

References

• Use keras to classify Sea Lions: 0.91 accuracy
• Get coordinates using blob detection