Update modules/segmentface.py

modules/segmentface.py

@@ -1,75 +1,72 @@
 import cv2
 import mediapipe as mp
 import numpy as np
-from rembg import remove  
-from PIL import Image  
+from rembg import remove
+from PIL import Image
+import io
 
 class FaceSegmenter:
     def __init__(self, threshold=0.5):
         self.threshold = threshold
-        # Initialize face detection
         self.face_detection = mp.solutions.face_detection.FaceDetection(
-            model_selection=1,  # 1 for general use, 0 for close-up faces
-            min_detection_confidence=0.5
+            model_selection=1, min_detection_confidence=0.5
         )
-        # Initialize selfie segmentation (for background removal)
         self.selfie_segmentation = mp.solutions.selfie_segmentation.SelfieSegmentation(
-            model_selection=1  # 1 for general use, 0 for close-up faces
+            model_selection=1
         )
 
-    def segment_face(self, image_path):
-        # Load the image
-        image = cv2.imread(image_path)
-        if image is None:
-            raise ValueError("Image not found or unable to load.")
+    def segment_face(self, image_input):
+        # Handle both file paths and numpy arrays
+        if isinstance(image_input, str):
+            # Load from file path
+            image = cv2.imread(image_input)
+            if image is None:
+                raise ValueError("Image not found or unable to load.")
+        elif isinstance(image_input, np.ndarray):
+            # Use numpy array directly (BGR format)
+            image = image_input.copy()
+        else:
+            raise ValueError("Input must be file path string or numpy array")
 
-        # Convert to RGB (MediaPipe requires RGB input)
         rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-        # Step 1: Detect the face
         face_results = self.face_detection.process(rgb_image)
+
         if not face_results.detections:
-            # Use rembg to remove the background
-            with open(image_path, "rb") as input_file:
-                input_image = input_file.read()
-                output_image = remove(input_image)  
-            # Convert the output image to a numpy array
-            output_image = np.array(Image.open(io.BytesIO(output_image)))
-            # Convert RGBA to RGB (remove alpha channel)
+            # Use rembg with numpy array input
+            pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+            output_image = remove(pil_image)  # rembg handles PIL Images
+            
+            # Convert to numpy array and remove alpha channel
+            output_image = np.array(output_image)
             if output_image.shape[2] == 4:
                 output_image = cv2.cvtColor(output_image, cv2.COLOR_RGBA2RGB)
             return output_image
 
-        # Get the bounding box of the first detected face
+        # Existing face segmentation logic
         detection = face_results.detections[0]
         bboxC = detection.location_data.relative_bounding_box
         h, w, _ = image.shape
-        x, y, width, height = int(bboxC.xmin * w), int(bboxC.ymin * h), \
-                              int(bboxC.width * w), int(bboxC.height * h)
+        x, y, width, height = (
+            int(bboxC.xmin * w), int(bboxC.ymin * h),
+            int(bboxC.width * w), int(bboxC.height * h)
+        )
 
-        # Step 2: Segment the foreground (selfie segmentation)
         segmentation_results = self.selfie_segmentation.process(rgb_image)
-        if segmentation_results.segmentation_mask is None:
-            raise ValueError("Segmentation failed.")
-
-        # Create a binary mask
         mask = (segmentation_results.segmentation_mask > self.threshold).astype(np.uint8)
-
-        # Step 3: Crop the face using the bounding box
+        
         face_mask = np.zeros_like(mask)
         face_mask[y:y+height, x:x+width] = mask[y:y+height, x:x+width]
-
-        # Apply the mask to the original image
         segmented_face = cv2.bitwise_and(image, image, mask=face_mask)
 
         return segmented_face
 
-    def save_segmented_face(self, image_path, output_path):
-        segmented_face = self.segment_face(image_path)
+    # Updated helper methods to handle numpy arrays
+    def save_segmented_face(self, image_input, output_path):
+        segmented_face = self.segment_face(image_input)
         cv2.imwrite(output_path, segmented_face)
 
-    def show_segmented_face(self, image_path):
-        segmented_face = self.segment_face(image_path)
+    def show_segmented_face(self, image_input):
+        segmented_face = self.segment_face(image_input)
         cv2.imshow("Segmented Face", segmented_face)
         cv2.waitKey(0)
         cv2.destroyAllWindows()