Add color and saturation filters to filters.py

- Introduced warm and cool color effect filters
- Added saturation adjustment filter
- Implemented intensity/factor controls for each filter
- Used OpenCV for color channel manipulation
- Registered new filters with default and range parameters

filters.py

@@ -143,3 +143,105 @@ def sketch_effect(image):
     sketch = cv2.divide(gray, 255 - blurred, scale=256)
 
     return sketch
+
+
+@registry.register("Warm", defaults={
+    "intensity": 30,
+}, min_vals={
+    "intensity": 0,
+}, max_vals={
+    "intensity": 100,
+}, step_vals={
+    "intensity": 1,
+})
+def warm_filter(image, intensity: int = 30):
+    """
+    ## Adds a warm color effect to the image.
+
+    **Args:**
+    * `image` (numpy.ndarray): Input image (BGR)
+    * `intensity` (int): Intensity of the warm effect (0-100)
+
+    **Returns:**
+    * `numpy.ndarray`: Image with warm color effect
+    """
+    # Convert intensity to actual adjustment values
+    intensity_scale = intensity / 100.0
+    
+    # Split the image into BGR channels
+    b, g, r = cv2.split(image.astype(np.float32))
+    
+    # Increase red, slightly increase green, decrease blue
+    r = np.clip(r * (1 + 0.5 * intensity_scale), 0, 255)
+    g = np.clip(g * (1 + 0.1 * intensity_scale), 0, 255)
+    b = np.clip(b * (1 - 0.1 * intensity_scale), 0, 255)
+    
+    return cv2.merge([b, g, r]).astype(np.uint8)
+
+
+@registry.register("Cool", defaults={
+    "intensity": 30,
+}, min_vals={
+    "intensity": 0,
+}, max_vals={
+    "intensity": 100,
+}, step_vals={
+    "intensity": 1,
+})
+def cool_filter(image, intensity: int = 30):
+    """
+    ## Adds a cool color effect to the image.
+
+    **Args:**
+    * `image` (numpy.ndarray): Input image (BGR)
+    * `intensity` (int): Intensity of the cool effect (0-100)
+
+    **Returns:**
+    * `numpy.ndarray`: Image with cool color effect
+    """
+    # Convert intensity to actual adjustment values
+    intensity_scale = intensity / 100.0
+    
+    # Split the image into BGR channels
+    b, g, r = cv2.split(image.astype(np.float32))
+    
+    # Increase blue, slightly increase green, decrease red
+    b = np.clip(b * (1 + 0.5 * intensity_scale), 0, 255)
+    g = np.clip(g * (1 + 0.1 * intensity_scale), 0, 255)
+    r = np.clip(r * (1 - 0.1 * intensity_scale), 0, 255)
+    
+    return cv2.merge([b, g, r]).astype(np.uint8)
+
+
+@registry.register("Saturation", defaults={
+    "factor": 50,
+}, min_vals={
+    "factor": 0,
+}, max_vals={
+    "factor": 100,
+}, step_vals={
+    "factor": 1,
+})
+def adjust_saturation(image, factor: int = 50):
+    """
+    ## Adjusts the saturation of the image.
+
+    **Args:**
+    * `image` (numpy.ndarray): Input image (BGR)
+    * `factor` (int): Saturation factor (0-100, 50 is normal)
+
+    **Returns:**
+    * `numpy.ndarray`: Image with adjusted saturation
+    """
+    # Convert factor to multiplication value (0.0 to 2.0)
+    factor = (factor / 50.0)
+    
+    # Convert to HSV
+    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV).astype(np.float32)
+    
+    # Adjust saturation
+    hsv[:, :, 1] = np.clip(hsv[:, :, 1] * factor, 0, 255)
+    
+    # Convert back to BGR
+    return cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
+