Update face_detection.py

face_detection.py

@@ -1,27 +1,24 @@
-# Copyright (c) 2021 Justin Pinkney
-
 import dlib
 import numpy as np
-import os
-from PIL import Image
-from PIL import ImageOps
+from PIL import Image, ImageOps
 from scipy.ndimage import gaussian_filter
 import cv2
 
-
 MODEL_PATH = "shape_predictor_5_face_landmarks.dat"
 detector = dlib.get_frontal_face_detector()
 
-
 def align(image_in, face_index=0, output_size=256):
     try:
         image_in = ImageOps.exif_transpose(image_in)
     except:
         print("exif problem, not rotating")
 
+    # Convert image to RGB format
+    image_in = ensure_rgb(np.array(image_in))
+
     landmarks = list(get_landmarks(image_in))
     n_faces = len(landmarks)
-    face_index = min(n_faces-1, face_index)
+    face_index = min(n_faces - 1, face_index)
     if n_faces == 0:
         aligned_image = image_in
         quad = None
@@ -30,7 +27,6 @@ def align(image_in, face_index=0, output_size=256):
 
     return aligned_image, n_faces, quad
 
-
 def composite_images(quad, img, output):
     """Composite an image into and output canvas according to transformed co-ords"""
     output = output.convert("RGBA")
@@ -43,98 +39,110 @@ def composite_images(quad, img, output):
     output.alpha_composite(img)
 
     return output.convert("RGB")
-    
 
 def get_landmarks(image):
-    """Get landmarks from PIL image"""
+    """Get landmarks from numpy image"""
     shape_predictor = dlib.shape_predictor(MODEL_PATH)
 
-    max_size = max(image.size)
-    reduction_scale = int(max_size/512)
+    max_size = max(image.shape[:2])
+    reduction_scale = int(max_size / 512)
     if reduction_scale == 0:
         reduction_scale = 1
-    downscaled = image.reduce(reduction_scale)
-    img = np.array(downscaled)
-    detections = detector(img, 0)
+    downscaled = cv2.resize(image, (image.shape[1] // reduction_scale, image.shape[0] // reduction_scale))
+
+    print(f"Type of downscaled image: {downscaled.dtype}, Shape: {downscaled.shape}")
+    if downscaled.dtype != np.uint8:
+        raise ValueError("Image is not in 8-bit format")
+
+    detections = detector(downscaled, 0)
     
     for detection in detections:
         try:
-            face_landmarks = [(reduction_scale*item.x, reduction_scale*item.y) for item in shape_predictor(img, detection).parts()]
+            face_landmarks = [(reduction_scale * item.x, reduction_scale * item.y) for item in shape_predictor(downscaled, detection).parts()]
             yield face_landmarks
         except Exception as e:
             print(e)
 
+def ensure_rgb(image):
+    if len(image.shape) == 2:  # If the image is grayscale
+        return cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+    elif image.shape[2] == 4:  # If the image has an alpha channel
+        return cv2.cvtColor(image, cv2.COLOR_BGRA2RGB)
+    elif image.shape[2] == 3:  # If the image is already RGB
+        return image
+    else:
+        raise ValueError("Unsupported image format")
 
 def image_align(src_img, face_landmarks, output_size=512, transform_size=2048, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False):
-        # Align function modified from ffhq-dataset
-        # See https://github.com/NVlabs/ffhq-dataset for license
-
-        lm = np.array(face_landmarks)
-        lm_eye_left      = lm[2:3]  # left-clockwise
-        lm_eye_right     = lm[0:1]  # left-clockwise
-
-        # Calculate auxiliary vectors.
-        eye_left     = np.mean(lm_eye_left, axis=0)
-        eye_right    = np.mean(lm_eye_right, axis=0)
-        eye_avg      = (eye_left + eye_right) * 0.5
-        eye_to_eye   = 0.71*(eye_right - eye_left)
-        mouth_avg    = lm[4]
-        eye_to_mouth = 1.35*(mouth_avg - eye_avg)
-
-        # Choose oriented crop rectangle.
-        x = eye_to_eye.copy()
-        x /= np.hypot(*x)
-        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-        x *= x_scale
-        y = np.flipud(x) * [-y_scale, y_scale]
-        c = eye_avg + eye_to_mouth * em_scale
-        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-        quad_orig = quad.copy()
-        qsize = np.hypot(*x) * 2        
-        
-        img = src_img.convert('RGBA').convert('RGB')
-
-        # Shrink.
-        shrink = int(np.floor(qsize / output_size * 0.5))
-        if shrink > 1:
-            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-            img = img.resize(rsize, Image.Resampling.LANCZOS)
-            quad /= shrink
-            qsize /= shrink
-
-        # Crop.
-        border = max(int(np.rint(qsize * 0.1)), 3)
-        crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
-        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
-        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-            img = img.crop(crop)
-            quad -= crop[0:2]
-
-        # Pad.
-        pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
-        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
-        if enable_padding and max(pad) > border - 4:
-            pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-            img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-            h, w, _ = img.shape
-            y, x, _ = np.ogrid[:h, :w, :1]
-            mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))
-            blur = qsize * 0.02
-            img += (gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-            img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
-            img = np.uint8(np.clip(np.rint(img), 0, 255))
-            if alpha:
-                mask = 1-np.clip(3.0 * mask, 0.0, 1.0)
-                mask = np.uint8(np.clip(np.rint(mask*255), 0, 255))
-                img = np.concatenate((img, mask), axis=2)
-                img = Image.fromarray(img, 'RGBA')
-            else:
-                img = Image.fromarray(img, 'RGB')
-            quad += pad[:2]
-
-        # Transform.
-        img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
-        if output_size < transform_size:
-            img = img.resize((output_size, output_size), Image.Resampling.LANCZOS)
-
-        return img, quad_orig
+    # Align function modified from ffhq-dataset
+    # See https://github.com/NVlabs/ffhq-dataset for license
+
+    lm = np.array(face_landmarks)
+    lm_eye_left = lm[2:3]  # left-clockwise
+    lm_eye_right = lm[0:1]  # left-clockwise
+
+    # Calculate auxiliary vectors.
+    eye_left = np.mean(lm_eye_left, axis=0)
+    eye_right = np.mean(lm_eye_right, axis=0)
+    eye_avg = (eye_left + eye_right) * 0.5
+    eye_to_eye = 0.71 * (eye_right - eye_left)
+    mouth_avg = lm[4]
+    eye_to_mouth = 1.35 * (mouth_avg - eye_avg)
+
+    # Choose oriented crop rectangle.
+    x = eye_to_eye.copy()
+    x /= np.hypot(*x)
+    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
+    x *= x_scale
+    y = np.flipud(x) * [-y_scale, y_scale]
+    c = eye_avg + eye_to_mouth * em_scale
+    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+    quad_orig = quad.copy()
+    qsize = np.hypot(*x) * 2
+
+    img = Image.fromarray(src_img).convert('RGBA').convert('RGB')
+
+    # Shrink.
+    shrink = int(np.floor(qsize / output_size * 0.5))
+    if shrink > 1:
+        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
+        img = img.resize(rsize, Image.Resampling.LANCZOS)
+        quad /= shrink
+        qsize /= shrink
+
+    # Crop.
+    border = max(int(np.rint(qsize * 0.1)), 3)
+    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
+    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
+    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
+        img = img.crop(crop)
+        quad -= crop[0:2]
+
+    # Pad.
+    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
+    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
+    if enable_padding and max(pad) > border - 4:
+        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
+        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+        h, w, _ = img.shape
+        y, x, _ = np.ogrid[:h, :w, :1]
+        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
+        blur = qsize * 0.02
+        img += (gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
+        img = np.uint8(np.clip(np.rint(img), 0, 255))
+        if alpha:
+            mask = 1 - np.clip(3.0 * mask, 0.0, 1.0)
+            mask = np.uint8(np.clip(np.rint(mask * 255), 0, 255))
+            img = np.concatenate((img, mask), axis=2)
+            img = Image.fromarray(img, 'RGBA')
+        else:
+            img = Image.fromarray(img, 'RGB')
+        quad += pad[:2]
+
+    # Transform.
+    img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
+    if output_size < transform_size:
+        img = img.resize((output_size, output_size), Image.Resampling.LANCZOS)
+
+    return img, quad_orig