thresholding-strategy

name	thresholding-strategy
description	Decision matrix for Global vs Otsu vs Adaptive thresholding, CLAHE parameter tuning, and histogram-based preprocessing
version	1.0.0
tags	["opencv","thresholding","histogram","CLAHE","segmentation","binary"]

Thresholding Strategy

When to Use This Skill

• Converting an image to binary (black/white) for segmentation
• Separating foreground objects from background
• Preprocessing before contour detection or morphological operations
• Improving contrast on low-contrast images
• Working with images that have uneven lighting or shadows

Decision Framework

Which Thresholding Method?

Image lighting conditions?
├── Uniform lighting, good contrast
│   ├── You know the ideal threshold value
│   │   └── ✅ Global Threshold (cv2.threshold)
│   └── You don't know the ideal value
│       └── ✅ Otsu's Method (automatic optimal threshold)
│
├── Uneven lighting / shadows present
│   └── ✅ Adaptive Threshold (cv2.adaptiveThreshold)
│       ├── General use → ADAPTIVE_THRESH_MEAN_C
│       └── Text/document → ADAPTIVE_THRESH_GAUSSIAN_C (better)
│
└── Very low contrast (details invisible)
    └── First enhance contrast, THEN threshold:
        ├── Moderate enhancement → Histogram Stretching
        ├── Strong enhancement → Histogram Equalization
        └── Local enhancement → CLAHE (best for most cases)

Contrast Enhancement Comparison

Method	Function	Effect	Best For
Histogram Stretching	cv2.normalize(NORM_MINMAX)	Linear rescale min→0, max→255	Natural-looking enhancement
Histogram Equalization	cv2.equalizeHist()	Aggressive CDF-based redistribution	Maximum contrast, looks artificial
CLAHE	cv2.createCLAHE()	Local adaptive equalization	Medical images, preserves local detail

Default choice: CLAHE. It provides strong enhancement without the over-amplification artifacts of global equalization.

Global Threshold Types

Type	Above Threshold	Below Threshold	Use Case
THRESH_BINARY	White (255)	Black (0)	Standard foreground extraction
THRESH_BINARY_INV	Black (0)	White (255)	Dark objects on light background
THRESH_TRUNC	Clamped to threshold	Unchanged	Brightness capping
THRESH_TOZERO	Unchanged	Set to 0	Keep only bright regions
THRESH_TOZERO_INV	Set to 0	Unchanged	Keep only dark regions

Critical Gotchas

1. Always Convert to Grayscale First

Thresholding only works on single-channel (grayscale) images.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

2. Otsu Automatically Finds the Best Threshold

Otsu minimizes intra-class variance to find the optimal split point. Pass 0 as threshold and add the flag:

# Otsu finds optimal threshold automatically
_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

3. Adaptive blockSize MUST Be Odd

# CRASHES
cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                      cv2.THRESH_BINARY, 10, 3)  # blockSize=10 → Error!

# CORRECT
cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                      cv2.THRESH_BINARY, 11, 3)  # blockSize=11 ✓

4. CLAHE Parameters — Tuning Guide

clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(8, 8))
result = clahe.apply(gray)

Parameter	Range	Effect
clipLimit	2.0 - 3.0 (typical)	Higher = more contrast, but more noise amplification
tileGridSize	(4,4) to (16,16)	Smaller tiles = more local adaptation, risk of artifacts

Safe defaults: clipLimit=2.5, tileGridSize=(8,8)

5. Histogram Equalization Only Works on Grayscale

cv2.equalizeHist() accepts only single-channel images. For color images, convert to LAB or YCrCb, equalize the L/Y channel only, then convert back.

6. Reading a Histogram for Quick Diagnostics

Histogram Shape	Diagnosis	Action
Clustered on left (dark)	Underexposed image	Apply CLAHE or equalization
Clustered on right (bright)	Overexposed image	Apply CLAHE or normalize
Narrow peak in center	Low contrast	Histogram stretching or CLAHE
Wide, uniform spread	Good contrast	Ready for thresholding
Two distinct peaks (bimodal)	Clear foreground/background	Otsu will work perfectly

Quick Reference

Histogram Computation

hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
# [gray]    — input image (list)
# [0]       — channel index
# None      — no mask
# [256]     — number of bins
# [0, 256]  — pixel value range

Complete Contrast Enhancement Pipeline

# Method 1: Histogram Stretching (natural)
stretched = cv2.normalize(gray, None, 0, 255, cv2.NORM_MINMAX)

# Method 2: Global Equalization (aggressive)
equalized = cv2.equalizeHist(gray)

# Method 3: CLAHE (recommended)
clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(8, 8))
enhanced = clahe.apply(gray)

Adaptive Thresholding

# MEAN_C: simple average of neighborhood
binary = cv2.adaptiveThreshold(gray, 255,
    cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 3)

# GAUSSIAN_C: weighted average (better for text)
binary = cv2.adaptiveThreshold(gray, 255,
    cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 3)
# blockSize=11: neighborhood size (must be odd)
# C=3: constant subtracted from computed mean