**Real-world analogy:** Computer vision for drones is like giving eyes and a brain to an aircraft. The camera captures what's there, but the CV pipeline interprets what it means—distinguishing a road from a river, a building from a tree.

## Image Preprocessing

Preprocessing normalizes raw camera data for consistent analysis across varying conditions.

**Real-world analogy:** Like adjusting your glasses and squinting in bright sunlight—preprocessing helps the vision system "see" clearly regardless of conditions.

```python
from scipy.ndimage import gaussian_filter, median_filter

class ImagePreprocessor:
    """Complete image preprocessing pipeline for drone imagery."""

    def __init__(self, target_size=(640, 480)):
        self.target_size = target_size

    def remove_lens_distortion(self, image, k1=-0.2, k2=0.05, p1=0.01, p2=-0.01):
        """
        Correct lens barrel/pincushion distortion.
        In production, use calibrated camera matrices.
        """
        h, w = image.shape[:2]
        cx, cy = w / 2, h / 2

        # Create coordinate grids
        y, x = np.mgrid[0:h, 0:w].astype(np.float32)
        x_norm = (x - cx) / cx
        y_norm = (y - cy) / cy

        # Radial distortion
        r2 = x_norm**2 + y_norm**2
        radial = 1 + k1 * r2 + k2 * r2**2

        # Apply distortion
        x_distorted = x_norm * radial + 2 * p1 * x_norm * y_norm + p2 * (r2 + 2 * x_norm**2)
        y_distorted = y_norm * radial + p1 * (r2 + 2 * y_norm**2) + 2 * p2 * x_norm * y_norm

        x_map = (x_distorted * cx + cx).astype(np.float32)
        y_map = (y_distorted * cy + cy).astype(np.float32)

        # Simple nearest-neighbor remapping (for demonstration)
        result = np.zeros_like(image)
        for i in range(h):
            for j in range(w):
                src_x = int(np.clip(x_map[i, j], 0, w-1))
                src_y = int(np.clip(y_map[i, j], 0, h-1))
                result[i, j] = image[src_y, src_x]

        return result

    def adaptive_histogram_equalization(self, image, clip_limit=2.0, grid_size=8):
        """Apply CLAHE (Contrast Limited Adaptive Histogram Equalization)."""
        h, w = image.shape[:2]
        grid_h, grid_w = h // grid_size, w // grid_size
        result = np.zeros_like(image)

        for i in range(grid_size):
            for j in range(grid_size):
                y_start, y_end = i * grid_h, (i + 1) * grid_h
                x_start, x_end = j * grid_w, (j + 1) * grid_w

                patch = image[y_start:y_end, x_start:x_end]
                if len(patch.shape) == 3:
                    patch = np.mean(patch, axis=2).astype(np.uint8)

                # Compute histogram
                hist, bins = np.histogram(patch.flatten(), 256, [0, 256])
                cdf = hist.cumsum()
                cdf_normalized = cdf * 255 / cdf[-1]

                # Equalize patch
                equalized = np.interp(patch.flatten(), bins[:-1], cdf_normalized)
                result[y_start:y_end, x_start:x_end] = equalized.reshape(patch.shape)

        return result.astype(np.uint8)

    def denoise_bilateral(self, image, sigma_space=10, sigma_color=25):
        """Bilateral filtering (edge-preserving denoising)."""
        # Simplified bilateral filter
        kernel_size = 5
        pad = kernel_size // 2
        padded = np.pad(image, ((pad, pad), (pad, pad)), mode='reflect')
        result = np.zeros_like(image, dtype=np.float64)

        for i in range(image.shape[0]):
            for j in range(image.shape[1]):
                patch = padded[i:i+kernel_size, j:j+kernel_size].astype(np.float64)
                center = float(padded[i + pad, j + pad])

                # Spatial weights
                y_coords, x_coords = np.mgrid[0:kernel_size, 0:kernel_size]
                spatial_w = np.exp(-((y_coords - pad)**2 + (x_coords - pad)**2) /
                                   (2 * sigma_space**2))

                # Range weights
                range_w = np.exp(-(patch - center)**2 / (2 * sigma_color**2))

                # Combined weights
                weights = spatial_w * range_w
                weights /= weights.sum()

                result[i, j] = np.sum(patch * weights)

        return result.astype(np.uint8)

    def motion_deblur(self, image, kernel_size=5):
        """Simple motion deblurring using Wiener filter approximation."""
        # Estimate blur kernel (simplified)
        kernel = np.ones((kernel_size, kernel_size)) / (kernel_size**2)

        # Frequency domain
        img_fft = np.fft.fft2(image.astype(np.float64))
        kernel_fft = np.fft.fft2(kernel, s=image.shape)

        # Wiener deconvolution
        K = 0.01  # Noise-to-signal ratio
        wiener = np.conj(kernel_fft) / (np.abs(kernel_fft)**2 + K)
        deblurred = np.real(np.fft.ifft2(img_fft * wiener))

        return np.clip(deblurred, 0, 255).astype(np.uint8)

# Example usage
np.random.seed(42)
# Simulate drone image (grayscale for simplicity)
image = np.random.randint(50, 200, (100, 100), dtype=np.uint8)

preprocessor = ImagePreprocessor()
equalized = preprocessor.adaptive_histogram_equalization(image)
denoised = preprocessor.denoise_bilateral(image)

print(f"Original: mean={image.mean():.1f}, std={image.std():.1f}")
print(f"Equalized: mean={equalized.mean():.1f}, std={equalized.std():.1f}")
print(f"Denoised: variance reduction={image.var() - denoised.var():.1f}")