Image Processing

import cv2
import numpy as np

# Read an image (OpenCV uses BGR by default)
img = cv2.imread("photo.jpg")
print(f"Shape: {img.shape}")       # (480, 640, 3)
print(f"Dtype: {img.dtype}")       # uint8
print(f"Size: {img.size} pixels") # 921600

import cv2

img = cv2.imread("photo.jpg")

# BGR to RGB (for matplotlib display)
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# BGR to Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print(f"Grayscale shape: {gray.shape}")  # (480, 640) - single channel

# BGR to HSV (useful for color detection)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# HSV color filtering - detect blue objects
lower_blue = np.array([100, 50, 50])
upper_blue = np.array([130, 255, 255])
mask = cv2.inRange(hsv, lower_blue, upper_blue)
result = cv2.bitwise_and(img, img, mask=mask)

import cv2

img = cv2.imread("photo.jpg")

# Resizing
resized = cv2.resize(img, (320, 240))                    # Exact size
resized = cv2.resize(img, None, fx=0.5, fy=0.5)          # Scale by 50%

# Cropping (NumPy slicing)
cropped = img[100:300, 200:500]  # rows 100-300, cols 200-500

# Rotating
h, w = img.shape[:2]
center = (w // 2, h // 2)
matrix = cv2.getRotationMatrix2D(center, angle=45, scale=1.0)
rotated = cv2.warpAffine(img, matrix, (w, h))

# Flipping
flipped_h = cv2.flip(img, 1)   # Horizontal flip
flipped_v = cv2.flip(img, 0)   # Vertical flip

# Average blur
blurred = cv2.blur(img, (5, 5))

# Gaussian blur (most common)
gaussian = cv2.GaussianBlur(img, (5, 5), 0)

# Median blur (good for salt-and-pepper noise)
median = cv2.medianBlur(img, 5)

# Bilateral filter (smooths while preserving edges)
bilateral = cv2.bilateralFilter(img, 9, 75, 75)

import cv2

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Sobel edge detection (directional)
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)  # Horizontal edges
sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)  # Vertical edges

# Canny edge detection (most popular)
edges = cv2.Canny(gray, threshold1=50, threshold2=150)

# Laplacian edge detection
laplacian = cv2.Laplacian(gray, cv2.CV_64F)

import numpy as np

# Sharpening kernel
kernel = np.array([[ 0, -1,  0],
                   [-1,  5, -1],
                   [ 0, -1,  0]])

sharpened = cv2.filter2D(img, -1, kernel)

import cv2
import numpy as np

# Create a structuring element
kernel = np.ones((5, 5), np.uint8)

# Erosion - shrinks white regions
eroded = cv2.erode(binary_img, kernel, iterations=1)

# Dilation - expands white regions
dilated = cv2.dilate(binary_img, kernel, iterations=1)

# Opening - erosion followed by dilation (removes noise)
opened = cv2.morphologyEx(binary_img, cv2.MORPH_OPEN, kernel)

# Closing - dilation followed by erosion (fills holes)
closed = cv2.morphologyEx(binary_img, cv2.MORPH_CLOSE, kernel)