utils

utils/alignment.py

@@ -0,0 +1,122 @@
+import numpy as np
+import PIL
+import PIL.Image
+import scipy
+import scipy.ndimage
+import dlib
+
+
+def get_landmark(filepath, predictor):
+    """get landmark with dlib
+    :return: np.array shape=(68, 2)
+    """
+    detector = dlib.get_frontal_face_detector()
+
+    img = dlib.load_rgb_image(filepath)
+    dets = detector(img, 2)
+
+    if len(dets) != 0:
+        max_area = 0
+        for k, d in enumerate(dets):
+            if d.area() > max_area or max_area == 0:
+                shape = predictor(img, d)
+                max_area = d.area()
+    else:
+        d = dlib.rectangle(0, 0, img.shape[1], img.shape[0])
+        shape = predictor(img, d)
+
+    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)
+
+    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
+    img = PIL.Image.open(filepath).convert("RGB")
+
+    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 aligned image.
+    return img

utils/mapper_utils.py

@@ -0,0 +1,49 @@
+import os
+
+
+google_drive_paths = {
+    "stylegan2-ffhq-config-f.pt": "https://drive.google.com/uc?id=1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT",
+
+    "mapper/pretrained/afro.pt": "https://drive.google.com/uc?id=1i5vAqo4z0I-Yon3FNft_YZOq7ClWayQJ",
+    "mapper/pretrained/angry.pt": "https://drive.google.com/uc?id=1g82HEH0jFDrcbCtn3M22gesWKfzWV_ma",
+    "mapper/pretrained/beyonce.pt": "https://drive.google.com/uc?id=1KJTc-h02LXs4zqCyo7pzCp0iWeO6T9fz",
+    "mapper/pretrained/bobcut.pt": "https://drive.google.com/uc?id=1IvyqjZzKS-vNdq_OhwapAcwrxgLAY8UF",
+    "mapper/pretrained/bowlcut.pt": "https://drive.google.com/uc?id=1xwdxI2YCewSt05dEHgkpmmzoauPjEnnZ",
+    "mapper/pretrained/curly_hair.pt": "https://drive.google.com/uc?id=1xZ7fFB12Ci6rUbUfaHPpo44xUFzpWQ6M",
+    "mapper/pretrained/depp.pt": "https://drive.google.com/uc?id=1FPiJkvFPG_y-bFanxLLP91wUKuy-l3IV",
+    "mapper/pretrained/hilary_clinton.pt": "https://drive.google.com/uc?id=1X7U2zj2lt0KFifIsTfOOzVZXqYyCWVll",
+    "mapper/pretrained/mohawk.pt": "https://drive.google.com/uc?id=1oMMPc8iQZ7dhyWavZ7VNWLwzf9aX4C09",
+    "mapper/pretrained/purple_hair.pt": "https://drive.google.com/uc?id=14H0CGXWxePrrKIYmZnDD2Ccs65EEww75",
+    "mapper/pretrained/surprised.pt": "https://drive.google.com/uc?id=1F-mPrhO-UeWrV1QYMZck63R43aLtPChI",
+    "mapper/pretrained/taylor_swift.pt": "https://drive.google.com/uc?id=10jHuHsKKJxuf3N0vgQbX_SMEQgFHDrZa",
+    "mapper/pretrained/trump.pt": "https://drive.google.com/uc?id=14v8D0uzy4tOyfBU3ca9T0AzTt3v-dNyh",
+    "mapper/pretrained/zuckerberg.pt": "https://drive.google.com/uc?id=1NjDcMUL8G-pO3i_9N6EPpQNXeMc3Ar1r",
+
+    "example_celebs.pt": "https://drive.google.com/uc?id=1VL3lP4avRhz75LxSza6jgDe-pHd2veQG"
+}
+
+
+def ensure_checkpoint_exists(model_weights_filename):
+    if not os.path.isfile(model_weights_filename) and (
+        model_weights_filename in google_drive_paths
+    ):
+        gdrive_url = google_drive_paths[model_weights_filename]
+        try:
+            from gdown import download as drive_download
+
+            drive_download(gdrive_url, model_weights_filename, quiet=False)
+        except ModuleNotFoundError:
+            print(
+                "gdown module not found.",
+                "pip3 install gdown or, manually download the checkpoint file:",
+                gdrive_url
+            )
+
+    if not os.path.isfile(model_weights_filename) and (
+        model_weights_filename not in google_drive_paths
+    ):
+        print(
+            model_weights_filename,
+            " not found, you may need to manually download the model weights."
+        )
+