Update vtoonify/model/encoder/align_all_parallel.py

vtoonify/model/encoder/align_all_parallel.py

@@ -1,217 +1,181 @@
-"""
-brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
-author: lzhbrian (https://lzhbrian.me)
-date: 2020.1.5
-note: code is heavily borrowed from 
-    https://github.com/NVlabs/ffhq-dataset
-    http://dlib.net/face_landmark_detection.py.html
-
-requirements:
-    apt install cmake
-    conda install Pillow numpy scipy
-    pip install dlib
-    # download face landmark model from:
-    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-"""
-from argparse import ArgumentParser
-import time
-import numpy as np
-import PIL
-import PIL.Image
-import os
-import scipy
-import scipy.ndimage
-import dlib
-import multiprocessing as mp
-import math
-
-#from configs.paths_config import model_paths
-SHAPE_PREDICTOR_PATH = 'shape_predictor_68_face_landmarks.dat'#model_paths["shape_predictor"]
-
-
-def get_landmark(filepath, predictor):
-    """get landmark with dlib
-    :return: np.array shape=(68, 2)
-    """
-    detector = dlib.get_frontal_face_detector()
-    if type(filepath) == str:
-        img = dlib.load_rgb_image(filepath)
-    else:
-        img = filepath
-    dets = detector(img, 1)
-    
-    if len(dets) == 0:
-        print('Error: no face detected!')
-        return None
-    
-    shape = None
-    for k, d in enumerate(dets):
-        shape = predictor(img, d)
-    
-    if shape is None:
-        print('Error: No face detected! If you are sure there are faces in your input, you may rerun the code several times until the face is detected. Sometimes the detector is unstable.')
-    t = list(shape.parts())
-    a = []
-    for tt in t:
-        a.append([tt.x, tt.y])
-    lm = np.array(a)
-    return lm
-
-
-def align_face(filepath, predictor):
-    """
-    :param filepath: str
-    :return: PIL Image
-    """
-
-    lm = get_landmark(filepath, predictor)
-    if lm is None:
-        return None    
-    
-    lm_chin = lm[0: 17]  # left-right
-    lm_eyebrow_left = lm[17: 22]  # left-right
-    lm_eyebrow_right = lm[22: 27]  # left-right
-    lm_nose = lm[27: 31]  # top-down
-    lm_nostrils = lm[31: 36]  # top-down
-    lm_eye_left = lm[36: 42]  # left-clockwise
-    lm_eye_right = lm[42: 48]  # left-clockwise
-    lm_mouth_outer = lm[48: 60]  # left-clockwise
-    lm_mouth_inner = lm[60: 68]  # 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 = eye_right - eye_left
-    mouth_left = lm_mouth_outer[0]
-    mouth_right = lm_mouth_outer[6]
-    mouth_avg = (mouth_left + mouth_right) * 0.5
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Choose oriented crop rectangle.
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    x /= np.hypot(*x)
-    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-    y = np.flipud(x) * [-1, 1]
-    c = eye_avg + eye_to_mouth * 0.1
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    qsize = np.hypot(*x) * 2
-
-    # read image
-    if type(filepath) == str:
-        img = PIL.Image.open(filepath)
-    else:
-        img = PIL.Image.fromarray(filepath)
-
-    output_size = 256
-    transform_size = 256
-    enable_padding = True
-
-    # 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, PIL.Image.ANTIALIAS)
-        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 += (scipy.ndimage.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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        quad += pad[:2]
-
-    # Transform.
-    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
-    if output_size < transform_size:
-        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
-
-    # Save aligned image.
-    return img
-
-
-def chunks(lst, n):
-    """Yield successive n-sized chunks from lst."""
-    for i in range(0, len(lst), n):
-        yield lst[i:i + n]
-
-
-def extract_on_paths(file_paths):
-    predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
-    pid = mp.current_process().name
-    print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
-    tot_count = len(file_paths)
-    count = 0
-    for file_path, res_path in file_paths:
-        count += 1
-        if count % 100 == 0:
-            print('{} done with {}/{}'.format(pid, count, tot_count))
-        try:
-            res = align_face(file_path, predictor)
-            res = res.convert('RGB')
-            os.makedirs(os.path.dirname(res_path), exist_ok=True)
-            res.save(res_path)
-        except Exception:
-            continue
-    print('\tDone!')
-
-
-def parse_args():
-    parser = ArgumentParser(add_help=False)
-    parser.add_argument('--num_threads', type=int, default=1)
-    parser.add_argument('--root_path', type=str, default='')
-    args = parser.parse_args()
-    return args
-
-
-def run(args):
-    root_path = args.root_path
-    out_crops_path = root_path + '_crops'
-    if not os.path.exists(out_crops_path):
-        os.makedirs(out_crops_path, exist_ok=True)
-
-    file_paths = []
-    for root, dirs, files in os.walk(root_path):
-        for file in files:
-            file_path = os.path.join(root, file)
-            fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
-            res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
-            if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
-                continue
-            file_paths.append((file_path, res_path))
-
-    file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
-    print(len(file_chunks))
-    pool = mp.Pool(args.num_threads)
-    print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
-    tic = time.time()
-    pool.map(extract_on_paths, file_chunks)
-    toc = time.time()
-    print('Mischief managed in {}s'.format(toc - tic))
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    run(args)
+from argparse import ArgumentParser
+import time
+import numpy as np
+import PIL
+import PIL.Image
+import os
+import scipy
+import scipy.ndimage
+import insightface
+import multiprocessing as mp
+import math
+
+def get_landmark(filepath, face_detector):
+    """get landmark with InsightFace
+    :return: np.array shape=(68, 2)
+    """
+    if isinstance(filepath, str):
+        img = PIL.Image.open(filepath)
+        img = np.array(img)
+    else:
+        img = filepath
+
+    faces = face_detector.get(img)
+    
+    if len(faces) == 0:
+        print('Error: no face detected!')
+        return None
+    
+    # Assume the first detected face is the target
+    face = faces[0]
+    lm = face.landmark_2d_106[:, :2]  # Use 106-point landmarks
+    return lm
+
+def align_face(filepath, face_detector):
+    """
+    :param filepath: str
+    :return: PIL Image
+    """
+    lm = get_landmark(filepath, face_detector)
+    if lm is None:
+        return None    
+    
+    # Use the same landmark indices as before
+    lm_eye_left = lm[36: 42]  # left-clockwise
+    lm_eye_right = lm[42: 48]  # left-clockwise
+    lm_mouth_outer = lm[48: 60]  # 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 = eye_right - eye_left
+    mouth_left = lm_mouth_outer[0]
+    mouth_right = lm_mouth_outer[6]
+    mouth_avg = (mouth_left + mouth_right) * 0.5
+    eye_to_mouth = mouth_avg - eye_avg
+
+    # Choose oriented crop rectangle.
+    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+    x /= np.hypot(*x)
+    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
+    y = np.flipud(x) * [-1, 1]
+    c = eye_avg + eye_to_mouth * 0.1
+    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+    qsize = np.hypot(*x) * 2
+
+    # read image
+    if isinstance(filepath, str):
+        img = PIL.Image.open(filepath)
+    else:
+        img = PIL.Image.fromarray(filepath)
+
+    output_size = 256
+    transform_size = 256
+    enable_padding = True
+
+    # 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, PIL.Image.ANTIALIAS)
+        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 += (scipy.ndimage.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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
+        quad += pad[:2]
+
+    # Transform.
+    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
+    if output_size < transform_size:
+        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
+
+    return img
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i:i + n]
+
+def extract_on_paths(file_paths, face_detector):
+    pid = mp.current_process().name
+    print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
+    tot_count = len(file_paths)
+    count = 0
+    for file_path, res_path in file_paths:
+        count += 1
+        if count % 100 == 0:
+            print('{} done with {}/{}'.format(pid, count, tot_count))
+        try:
+            res = align_face(file_path, face_detector)
+            res = res.convert('RGB')
+            os.makedirs(os.path.dirname(res_path), exist_ok=True)
+            res.save(res_path)
+        except Exception:
+            continue
+    print('\tDone!')
+
+def parse_args():
+    parser = ArgumentParser(add_help=False)
+    parser.add_argument('--num_threads', type=int, default=1)
+    parser.add_argument('--root_path', type=str, default='')
+    args = parser.parse_args()
+    return args
+
+def run(args):
+    root_path = args.root_path
+    out_crops_path = root_path + '_crops'
+    if not os.path.exists(out_crops_path):
+        os.makedirs(out_crops_path, exist_ok=True)
+
+    file_paths = []
+    for root, dirs, files in os.walk(root_path):
+        for file in files:
+            file_path = os.path.join(root, file)
+            fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
+            res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
+            if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
+                continue
+            file_paths.append((file_path, res_path))
+
+    file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
+    print(len(file_chunks))
+    pool = mp.Pool(args.num_threads)
+    print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
+    tic = time.time()
+    pool.starmap(extract_on_paths, [(chunk, face_detector) for chunk in file_chunks])
+    toc = time.time()
+    print('Mischief managed in {}s'.format(toc - tic))
+
+if __name__ == '__main__':
+    # Initialize InsightFace
+    face_detector = insightface.app.FaceAnalysis()
+    face_detector.prepare(ctx_id=-1, det_size=(640, 640))  # ctx_id=-1 for CPU
+
+    args = parse_args()
+    run(args)