Computer Vision Mastery: From Image Processing to Deep Learning

🎯 Classification

What is in this image?

• Image recognition
• Medical diagnosis
• Quality control

📍 Detection

Where are objects located?

• Autonomous vehicles
• Security systems
• Retail analytics

import cv2
import numpy as np
import matplotlib.pyplot as plt

class ImageProcessor:
    def __init__(self):
        self.original_image = None
        self.processed_image = None
    
    def load_image(self, image_path):
        """Load image from file"""
        self.original_image = cv2.imread(image_path)
        if self.original_image is None:
            raise ValueError(f"Could not load image: {image_path}")
        return self.original_image
    
    def convert_to_grayscale(self):
        """Convert image to grayscale"""
        if self.original_image is None:
            raise ValueError("No image loaded")
        self.processed_image = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
        return self.processed_image
    
    def apply_gaussian_blur(self, kernel_size=5):
        """Apply Gaussian blur to reduce noise"""
        if self.processed_image is None:
            self.convert_to_grayscale()
        
        self.processed_image = cv2.GaussianBlur(
            self.processed_image, 
            (kernel_size, kernel_size), 
            0
        )
        return self.processed_image
    
    def detect_edges(self, low_threshold=50, high_threshold=150):
        """Detect edges using Canny edge detector"""
        if self.processed_image is None:
            self.apply_gaussian_blur()
        
        edges = cv2.Canny(
            self.processed_image,
            low_threshold,
            high_threshold
        )
        return edges
    
    def find_contours(self, edges):
        """Find contours in edge image"""
        contours, hierarchy = cv2.findContours(
            edges,
            cv2.RETR_EXTERNAL,
            cv2.CHAIN_APPROX_SIMPLE
        )
        return contours, hierarchy
    
    def display_results(self, edges, contours):
        """Display original, edges, and contours"""
        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
        
        # Original image
        axes[0].imshow(cv2.cvtColor(self.original_image, cv2.COLOR_BGR2RGB))
        axes[0].set_title('Original Image')
        axes[0].axis('off')
        
        # Edge detection
        axes[1].imshow(edges, cmap='gray')
        axes[1].set_title('Edge Detection')
        axes[1].axis('off')
        
        # Contours
        contour_image = self.original_image.copy()
        cv2.drawContours(contour_image, contours, -1, (0, 255, 0), 2)
        axes[2].imshow(cv2.cvtColor(contour_image, cv2.COLOR_BGR2RGB))
        axes[2].set_title('Detected Contours')
        axes[2].axis('off')
        
        plt.tight_layout()
        plt.show()

# Usage example
processor = ImageProcessor()
# processor.load_image('example.jpg')
# edges = processor.detect_edges()
# contours, _ = processor.find_contours(edges)
# processor.display_results(edges, contours)

Q: What's the difference between computer vision and image processing?

A: Image processing focuses on manipulating images (filtering, enhancement), while computer vision aims to understand and extract meaningful information from images, often involving machine learning.

Q: Do I need a powerful GPU for computer vision?

A: For traditional computer vision with OpenCV, CPU is sufficient. For deep learning models, GPU acceleration significantly speeds up training and inference, especially for large datasets.

Q: How much data do I need for computer vision projects?

A: It depends on the complexity. Simple tasks might need hundreds of images, while complex deep learning models typically require thousands to millions of labeled images.

Q: What career opportunities exist in computer vision?

A: Many opportunities exist in autonomous vehicles, medical imaging, robotics, security systems, retail analytics, and augmented reality. Average salaries range from $90,000 to $200,000+.