aeren23 Contour Analysis
When to Use This Skill
- Counting objects in an image
- Measuring object area, perimeter, or physical dimensions
- Classifying shapes (round vs elongated, regular vs irregular)
- Medical image analysis (tumor shape assessment)
- Drawing bounding boxes or centroids on detected objects
Decision Framework
Shape Metric Selection
| Metric | Formula | Range | Tells You |
|---|---|---|---|
| Aspect Ratio | width / height | 0→∞ | Shape elongation (1.0 = square/circle) |
| Extent | Object Area / Bounding Box Area | 0→1.0 | How much the box is filled |
| Solidity | Object Area / Convex Hull Area | 0→1.0 | Surface regularity (1.0 = smooth, <1.0 = irregular) |
| Eccentricity | Minor Axis / Major Axis | 0→1.0 | Circularity (0 = circle, 1 = line) |
Medical Application — Tumor Shape Classification
Solidity value?
├── ≈ 1.0 (smooth, convex surface)
│ └── Likely BENIGN — regular, well-defined boundary
│
└── << 1.0 (irregular, spiculated surface)
└── Likely MALIGNANT — irregular projections, infiltrative margin
Solidity is a gold-standard feature in medical image analysis for distinguishing benign vs malignant masses. Convex Hull wraps the object tightly — if the actual area is much smaller than the hull, the surface has indentations/spikes (suspicious morphology).
Contour Finding Parameters
| Parameter | Recommended | Alternative | When |
|---|---|---|---|
| Mode | RETR_EXTERNAL |
RETR_TREE |
External = outer boundaries only. Tree = nested hierarchy |
| Method | CHAIN_APPROX_SIMPLE |
CHAIN_APPROX_NONE |
Simple = corner points only (saves memory). None = all boundary pixels |
Critical Gotchas
1. Image MUST Be Binary Before findContours
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
2. findContours Expects White Objects on Black Background
If your objects are dark on a light background, use THRESH_BINARY_INV to invert.
3. drawContours Index: -1 = All
cv2.drawContours(img, contours, -1, (0, 255, 0), 2) # All contours
cv2.drawContours(img, contours, 0, (0, 255, 0), 2) # Only first contour
4. Minimum Points for fitEllipse
cv2.fitEllipse() requires at least 5 points in the contour. Filter small contours first.
5. Physical Measurement Requires Calibration
Pixel area alone is meaningless in real units. You need:
Real Area (mm²) = Pixel Area × (Physical Size of 1 Pixel in mm)²
Quick Reference
Complete Contour Analysis Pipeline
import cv2
import numpy as np
img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
area = cv2.contourArea(cnt)
perimeter = cv2.arcLength(cnt, True)
x, y, w, h = cv2.boundingRect(cnt)
# Aspect Ratio
aspect_ratio = w / h
# Extent
rect_area = w * h
extent = area / rect_area if rect_area > 0 else 0
# Solidity
hull = cv2.convexHull(cnt)
hull_area = cv2.contourArea(hull)
solidity = area / hull_area if hull_area > 0 else 0
# Centroid
M = cv2.moments(cnt)
if M['m00'] > 0:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
Eccentricity via Ellipse Fitting
if len(cnt) >= 5:
ellipse = cv2.fitEllipse(cnt)
(center, (minor_axis, major_axis), angle) = ellipse
eccentricity = minor_axis / major_axis # 0=circle, 1=line
Scikit-Image Alternative (37 Features in One Call)
from skimage.measure import label, regionprops
labeled = label(binary_image)
for region in regionprops(labeled):
print(region.area, region.perimeter, region.solidity,
region.eccentricity, region.centroid, region.bbox)
skimage.regionprops extracts 37 morphometric features per object in a single call, compared to manual per-feature computation in OpenCV.