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.

python
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:

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

3. Adaptive blockSize MUST Be Odd

python
# 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

python
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

python
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

python
# 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

python
# 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