Image Fundamentals

When to Use This Skill

• Starting any new image processing pipeline
• Choosing between color spaces (BGR, RGB, HSV, Gray, LAB)
• Deciding image format for saving/loading
• Debugging coordinate-related bugs in OpenCV
• Calculating memory requirements for image data

Decision Framework

Color Space Selection

| Goal | Convert To | OpenCV Code | Why | |------|-----------|-------------|-----| | Display with matplotlib | RGB | cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | Matplotlib expects RGB, OpenCV loads BGR | | Grayscale processing | Gray | cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | Single channel, faster computation | | Color-based object filtering | HSV | cv2.cvtColor(img, cv2.COLOR_BGR2HSV) | Isolate hue independently from brightness | | Perceptual color difference | LAB | cv2.cvtColor(img, cv2.COLOR_BGR2LAB) | L=lightness, A/B=color, perceptually uniform | | Print/publishing output | CMYK | External library | Subtractive color model for ink |

Format Selection

| Format | Use When | Compression | Quality Loss | |--------|----------|-------------|-------------| | .jpg/.jpeg | Web, general photos | Lossy | Yes — artifacts at low quality | | .png | Transparency needed, lossless required | Lossless | No | | .tiff | Medical/scientific, archival | Both | Depends on setting | | .dcm (DICOM) | Clinical X-ray, MR, CT | Lossless | No | | .nii (NIfTI) | Neuroimaging (brain MRI) | Lossless | No | | .gif | Animations, indexed color | Lossless (256 colors) | Color palette limited |

Bit Depth Decision

| Bit Depth | Colors | Use Case | Memory per Pixel | |-----------|--------|----------|------------------| | 1-bit | 2 (B/W) | Binary masks, documents | 0.125 bytes | | 8-bit gray | 256 shades | Standard grayscale processing | 1 byte | | 24-bit (3×8) | 16.7M | Standard color (BGR/RGB) | 3 bytes | | 32-bit float | Continuous | HDR, scientific computation | 4 bytes | | 48-bit (3×16) | 281T | Medical, raw camera | 6 bytes |

Memory formula: width × height × channels × bytes_per_channel

Example: 1920×1080 RGB (8-bit) = 1920 × 1080 × 3 × 1 = 6,220,800 bytes ≈ 5.93 MB in RAM

> Note: Compressed file size (JPG/PNG on disk) is much smaller, but OpenCV always decompresses to full size in memory.

Critical Gotchas

1. The BGR Trap (Most Common OpenCV Bug)

OpenCV loads images in BGR order, not RGB. If you display with matplotlib or send to a model expecting RGB, colors will be swapped (blue shirt appears red).

python
# WRONG — colors will be inverted
plt.imshow(cv2.imread('photo.jpg'))

# CORRECT
img = cv2.imread('photo.jpg')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

2. The Triple Coordinate Convention

OpenCV uses THREE different coordinate orders depending on context:

| Operation | Convention | Example | |-----------|------------|----------| | Array indexing | [y, x] (row, col) | pixel = img[200, 150] | | Drawing functions | (x, y) (col, row) | cv2.circle(img, (150, 200), 5, ...) | | Resize function | (width, height) = (x, y) | cv2.resize(img, (640, 480)) | | Shape property | (height, width, channels) | h, w, c = img.shape |

> This is the #1 source of coordinate bugs. When debugging spatial issues, always check which convention the function expects.

3. Grayscale Reading Shortcut

python
# These are equivalent:
gray = cv2.cvtColor(cv2.imread('img.jpg'), cv2.COLOR_BGR2GRAY)
gray = cv2.imread('img.jpg', 0)  # 0 flag = grayscale directly

4. Data Type Is Always uint8

OpenCV images default to uint8 (0-255). If you do math that overflows:

• OpenCV functions (cv2.add): clips to 0-255 (safe)
• NumPy operations (img1 + img2): wraps around (255+1=0, dangerous!)

python
# SAFE — clips at 255
result = cv2.add(img1, img2)

# DANGEROUS — wraps around
result = img1 + img2  # 200 + 100 = 44 (not 255!)

Quick Reference

Image I/O

python
img = cv2.imread('file.jpg')          # Load BGR
img = cv2.imread('file.jpg', 0)       # Load grayscale
cv2.imwrite('out.png', img)           # Save (format from extension)
cv2.imshow('Window', img)             # Display
cv2.waitKey(0)                        # Wait for keypress
cv2.destroyAllWindows()               # Clean up windows

ROI (Region of Interest)

python
roi = img[y1:y2, x1:x2]              # Crop — note: [rows, cols]
img[y1:y2, x1:x2] = 0                # Set region to black

Shape and Size

python
h, w, c = img.shape                   # Height, Width, Channels
total_pixels = img.size                # Total elements
dtype = img.dtype                      # Usually uint8

HSV Value Ranges in OpenCV

| Channel | Range | Notes | |---------|-------|-------| | H (Hue) | 0-179 | NOT 0-360! Divided by 2 to fit uint8 | | S (Saturation) | 0-255 | 0=gray/pastel, 255=vivid | | V (Value/Brightness) | 0-255 | 0=black, 255=brightest |

Common HSV hue ranges:

• Red: 0-15 AND 160-179 (wraps around!)
• Green: 45-90
• Blue: 105-135
• Yellow: 25-45

> Red wraps around the hue circle. You need TWO masks combined with OR to detect red objects in HSV.