Update face_detection.py

face_detection.py

@@ -1,17 +1,14 @@
-# Copyright (c) 2021 Justin Pinkney
-
-import dlib
+import insightface
 import numpy as np
-import os
-from PIL import Image
-from PIL import ImageOps
-from scipy.ndimage import gaussian_filter
+from PIL import Image, ImageOps
 import cv2
 
+# Load InsightFace models
+detector = insightface.model_zoo.get_model('retinaface_r50_v1')
+detector.prepare(ctx_id=-1)  # Use CPU, set ctx_id=0 for GPU
 
-MODEL_PATH = "shape_predictor_5_face_landmarks.dat"
-detector = dlib.get_frontal_face_detector()
-
+landmark_model = insightface.model_zoo.get_model('2d106det')
+landmark_model.prepare(ctx_id=-1)  # Use CPU, set ctx_id=0 for GPU
 
 def align(image_in, face_index=0, output_size=256):
     try:
@@ -30,111 +27,86 @@ 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")
-    img = img.convert("RGBA")
-    input_size = img.size
-    src = np.array(((0, 0), (0, input_size[1]), input_size, (input_size[0], 0)), dtype=np.float32)
-    dst = np.float32(quad)
-    mtx = cv2.getPerspectiveTransform(dst, src)
-    img = img.transform(output.size, Image.PERSPECTIVE, mtx.flatten(), Image.BILINEAR)
-    output.alpha_composite(img)
-
-    return output.convert("RGB")
-    
-
 def get_landmarks(image):
     """Get landmarks from PIL image"""
-    shape_predictor = dlib.shape_predictor(MODEL_PATH)
-
-    max_size = max(image.size)
-    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)
-    
-    for detection in detections:
-        try:
-            face_landmarks = [(reduction_scale*item.x, reduction_scale*item.y) for item in shape_predictor(img, detection).parts()]
-            yield face_landmarks
-        except Exception as e:
-            print(e)
+    img = np.array(image)
+    bboxes, _ = detector.detect(img, threshold=0.5, scale=1.0)
 
+    for bbox in bboxes:
+        # Use the landmark model to predict landmarks
+        landmarks = landmark_model.get(img, bbox)
+        yield landmarks
 
 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 = 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