utils

utils/drive.py

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+# URL helpers, see https://github.com/NVlabs/stylegan
+# ------------------------------------------------------------------------------------------
+
+import requests
+import html
+import hashlib
+import gdown
+import glob
+import os
+import io
+from typing import Any
+import re
+import uuid
+
+weight_dic = {'afhqwild.pt': 'https://drive.google.com/file/d/14OnzO4QWaAytKXVqcfWo_o2MzoR4ygnr/view?usp=sharing',
+              'afhqdog.pt': 'https://drive.google.com/file/d/16v6jPtKVlvq8rg2Sdi3-R9qZEVDgvvEA/view?usp=sharing',
+              'afhqcat.pt': 'https://drive.google.com/file/d/1HXLER5R3EMI8DSYDBZafoqpX4EtyOf2R/view?usp=sharing',
+              'ffhq.pt': 'https://drive.google.com/file/d/1AT6bNR2ppK8f2ETL_evT27f3R_oyWNHS/view?usp=sharing',
+              'metfaces.pt': 'https://drive.google.com/file/d/16wM2PwVWzaMsRgPExvRGsq6BWw_muKbf/view?usp=sharing',
+              'seg.pth': 'https://drive.google.com/file/d/1lIKvQaFKHT5zC7uS4p17O9ZpfwmwlS62/view?usp=sharing'}
+
+
+def download_weight(weight_path):
+    gdown.download(weight_dic[os.path.basename(weight_path)],
+                   output=weight_path, fuzzy=True)
+
+
+def is_url(obj: Any) -> bool:
+    """Determine whether the given object is a valid URL string."""
+    if not isinstance(obj, str) or not "://" in obj:
+        return False
+    try:
+        res = requests.compat.urlparse(obj)
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+    except:
+        return False
+    return True
+
+
+def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True,
+             return_path: bool = False) -> Any:
+    """Download the given URL and return a binary-mode file object to access the data."""
+    assert is_url(url)
+    assert num_attempts >= 1
+
+    # Lookup from cache.
+    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
+    if cache_dir is not None:
+        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
+        if len(cache_files) == 1:
+            if (return_path):
+                return cache_files[0]
+            else:
+                return open(cache_files[0], "rb")
+
+    # Download.
+    url_name = None
+    url_data = None
+    with requests.Session() as session:
+        if verbose:
+            print("Downloading %s ..." % url, end="", flush=True)
+        for attempts_left in reversed(range(num_attempts)):
+            try:
+                with session.get(url) as res:
+                    res.raise_for_status()
+                    if len(res.content) == 0:
+                        raise IOError("No data received")
+
+                    if len(res.content) < 8192:
+                        content_str = res.content.decode("utf-8")
+                        if "download_warning" in res.headers.get("Set-Cookie", ""):
+                            links = [html.unescape(link) for link in content_str.split('"') if
+                                     "export=download" in link]
+                            if len(links) == 1:
+                                url = requests.compat.urljoin(url, links[0])
+                                raise IOError("Google Drive virus checker nag")
+                        if "Google Drive - Quota exceeded" in content_str:
+                            raise IOError("Google Drive quota exceeded")
+
+                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
+                    url_name = match[1] if match else url
+                    url_data = res.content
+                    if verbose:
+                        print(" done")
+                    break
+            except:
+                if not attempts_left:
+                    if verbose:
+                        print(" failed")
+                    raise
+                if verbose:
+                    print(".", end="", flush=True)
+
+    # Save to cache.
+    if cache_dir is not None:
+        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
+        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
+        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
+        os.makedirs(cache_dir, exist_ok=True)
+        with open(temp_file, "wb") as f:
+            f.write(url_data)
+        os.replace(temp_file, cache_file)  # atomic
+        if (return_path): return cache_file
+
+    # Return data as file object.
+    return io.BytesIO(url_data)

utils/shape_predictor.py

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+import os
+from pathlib import Path
+
+import PIL
+import dlib
+import numpy as np
+import scipy
+import scipy.ndimage
+import torch
+from PIL import Image
+from torchvision import transforms as T
+
+from utils.drive import open_url
+
+"""
+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
+"""
+
+
+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, 1)
+    filepath = Path(filepath)
+    print(f"{filepath.name}: Number of faces detected: {len(dets)}")
+    shapes = [predictor(img, d) for k, d in enumerate(dets)]
+
+    lms = [np.array([[tt.x, tt.y] for tt in shape.parts()]) for shape in shapes]
+
+    return lms
+
+
+def get_landmark_from_tensors(tensors: list[torch.Tensor | Image.Image | np.ndarray], predictor):
+    detector = dlib.get_frontal_face_detector()
+    transform = T.ToPILImage()
+    images = []
+    lms = []
+
+    for k, tensor in enumerate(tensors):
+        if isinstance(tensor, torch.Tensor):
+            img_pil = transform(tensor)
+        else:
+            img_pil = tensor
+        img = np.array(img_pil)
+        images.append(img_pil)
+
+        dets = detector(img, 1)
+        if len(dets) == 0:
+            raise ValueError(f"No faces detected in the image {k}.")
+        elif len(dets) == 1:
+            print(f"Number of faces detected: {len(dets)}")
+        else:
+            print(f"Number of faces detected: {len(dets)}, get largest face")
+
+        # Find the largest face
+        dets = sorted(dets, key=lambda det: det.width() * det.height(), reverse=True)
+        shape = predictor(img, dets[0])
+        lm = np.array([[tt.x, tt.y] for tt in shape.parts()])
+        lms.append(lm)
+
+    return images, lms
+
+
+def align_face(data, predictor=None, is_filepath=False, return_tensors=True):
+    """
+    :param data: filepath or list torch Tensors
+    :return: list of PIL Images
+    """
+    if predictor is None:
+        predictor_path = 'shape_predictor_68_face_landmarks.dat'
+
+        if not os.path.isfile(predictor_path):
+            print("Downloading Shape Predictor")
+            data_io = open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx")
+            with open(predictor_path, 'wb') as f:
+                f.write(data_io.getbuffer())
+
+        predictor = dlib.shape_predictor(predictor_path)
+
+    if is_filepath:
+        lms = get_landmark(data, predictor)
+    else:
+        if not isinstance(data, list):
+            data = [data]
+        images, lms = get_landmark_from_tensors(data, predictor)
+
+    imgs = []
+    for num_img, lm in enumerate(lms):
+        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 is_filepath:
+            img = PIL.Image.open(data)
+        else:
+            img = images[num_img]
+
+        output_size = 1024
+        # output_size = 256
+        transform_size = 4096
+        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.LANCZOS)
+
+        # Save aligned image.
+        imgs.append(img)
+
+    if return_tensors:
+        transform = T.ToTensor()
+        tensors = [transform(img).clamp(0, 1) for img in imgs]
+        return tensors
+    return imgs