Practice: CNN Vision Filter Design

# Practice: Work with images as number grids

def create_gradient_image(width, height):
    """Create a gradient image from left (dark) to right (bright)."""
    image = []
    for row in range(height):
        r = []
        for col in range(width):
            pixel = int(col / (width - 1) * 255)
            r.append(pixel)
        image.append(r)
    return image

def image_stats(image):
    """Calculate basic statistics about an image."""
    flat = [px for row in image for px in row]
    total = len(flat)
    total_brightness = sum(flat)
    return {
        "width": len(image[0]),
        "height": len(image),
        "total_pixels": total,
        "min_brightness": min(flat),
        "max_brightness": max(flat),
        "avg_brightness": total_brightness / total,
    }

# Create and analyze a gradient image
gradient = create_gradient_image(10, 5)

print("Gradient Image (0=dark, 255=bright):")
for row in gradient:
    visual = "".join(
        " " if px < 85 else ("." if px < 170 else "#")
        for px in row
    )
    print(f"  |{visual}|")

print()
stats = image_stats(gradient)
print("Image Statistics:")
for key, value in stats.items():
    print(f"  {key}: {value if isinstance(value, int) else f'{value:.1f}'}")

print()

# Apply a simple brightness adjustment
factor = 0.7  # darken
darkened = [[int(px * factor) for px in row] for row in gradient]

print(f"After darkening (factor={factor}):")
for row in darkened:
    visual = "".join(
        " " if px < 85 else ("." if px < 170 else "#")
        for px in row
    )
    print(f"  |{visual}|")