Remove enhance_image4 function and its usage from enhance_image_command

main.py

@@ -357,71 +357,6 @@ def enhance_image3(image: bytes) -> bytes:
     return enhanced_webp.tobytes()
 
 
-def enhance_image4(image: bytes) -> bytes:  # noqa: PLR0914
-    """Enhance an image using a simplified Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to better reveal details in dark areas.
-
-    This approach first denoises the image, then applies multi-scale retinex processing to
-    boost local contrast. A color restoration step helps maintain a natural look.
-
-    Args:
-        image (bytes): The input image to enhance.
-
-    Returns:
-        bytes: The enhanced image encoded in WebP format.
-    """  # noqa: E501
-    # Decode the image from bytes
-    nparr = np.frombuffer(image, np.uint8)
-    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    # Denoise the image with conservative settings
-    img = cv2.fastNlMeansDenoisingColored(img, None, 5, 5, 7, 21)
-
-    # Convert to float64 and add 1 to avoid log(0)
-    img = img.astype(np.float64) + 1.0
-
-    # Define scales for the multi-scale retinex (you can experiment with these)
-    scales = [15, 80, 250]
-    retinex = np.zeros_like(img)
-
-    # Compute the retinex output over different scales
-    for sigma in scales:
-        # Gaussian blur with standard deviation sigma; kernel size is computed automatically
-        blur = cv2.GaussianBlur(img, (0, 0), sigma)
-        retinex += np.log(img) - np.log(blur)
-
-    # Average the retinex result over all scales
-    retinex /= len(scales)
-
-    # --- Color Restoration Step ---
-    # Compute the sum across color channels (with a small epsilon to avoid division by zero)
-    eps = 1e-6
-    sum_channels = np.sum(img, axis=2, keepdims=True) + eps
-
-    # The color restoration factor; alpha is chosen empirically (here 125 works well in many cases)
-    color_restoration = np.log(125 * img / sum_channels + 1)
-
-    # Combine the retinex output with the color restoration factor
-    msrcr = retinex * color_restoration
-
-    # Apply gain and offset adjustments to fine-tune brightness and contrast
-    gain = 1.5
-    offset = 20
-    msrcr = msrcr * gain + offset
-
-    # Normalize each channel to span the full 0-255 range
-    for channel in range(3):
-        ch_data = msrcr[:, :, channel]
-        ch_min, ch_max = ch_data.min(), ch_data.max()
-        msrcr[:, :, channel] = ((ch_data - ch_min) / (ch_max - ch_min + eps)) * 255
-
-    # Clip the values to valid 8-bit range and convert back to uint8
-    enhanced_img = np.clip(msrcr, 0, 255).astype(np.uint8)
-
-    # Encode the enhanced image to WebP format
-    _, enhanced_webp = cv2.imencode(".webp", enhanced_img)
-    return enhanced_webp.tobytes()
-
-
 @client.tree.context_menu(name="Enhance Image")
 @app_commands.allowed_installs(guilds=True, users=True)
 @app_commands.allowed_contexts(guilds=True, dms=True, private_channels=True)
@@ -453,10 +388,7 @@ async def enhance_image_command(interaction: discord.Interaction, message: disco
         enhanced_image3: bytes = enhance_image3(image_bytes)
         file3 = discord.File(fp=io.BytesIO(enhanced_image3), filename=f"enhanced3-{timestamp}.webp")
 
-        enhanced_image4: bytes = enhance_image4(image_bytes)
+        await interaction.followup.send("Enhanced version:", files=[file1, file2, file3])
-        file4 = discord.File(fp=io.BytesIO(enhanced_image4), filename=f"enhanced4-{timestamp}.webp")
-
-        await interaction.followup.send("Enhanced version:", files=[file1, file2, file3, file4])
 
     except (httpx.HTTPError, openai.OpenAIError) as e:
         logger.exception("Failed to enhance image")