``

Author: Alex Damian

.gitignore

@@ -1,5 +1,7 @@
 .DS_Store
+__pycache__/*
 .idea/*
 runs/*
 input/*
-cache/*
+cache/*
+realpics/*

align_face.py

@@ -0,0 +1,167 @@
+"""
+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
+"""
+
+import numpy as np
+import PIL
+import PIL.Image
+import sys
+import os
+import glob
+import scipy
+import scipy.ndimage
+import dlib
+from drive import open_url
+from pathlib import Path
+import argparse
+from bicubic import BicubicDownSample
+import torchvision
+
+def get_landmark(filepath):
+    """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 align_face(filepath):
+    """
+    :param filepath: str
+    :return: PIL Image
+    """
+
+    lms = get_landmark(filepath)
+    imgs=[]
+    for lm in 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
+        img = PIL.Image.open(filepath)
+
+        output_size=1024
+        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.ANTIALIAS)
+
+        # Save aligned image.
+        imgs.append(img)
+    return imgs
+
+parser = argparse.ArgumentParser(description='PULSE')
+
+parser.add_argument('-input_dir', type=str, default='realpics', help='directory with unprocessed images')
+parser.add_argument('-output_dir', type=str, default='input', help='output directory')
+parser.add_argument('-output_size', type=int, default=32, help='size to downscale the input images to, must be power of 2')
+parser.add_argument('-seed', type=int, help='manual seed to use')
+parser.add_argument('-cache_dir', type=str, default='cache', help='cache directory for model weights')
+
+args = parser.parse_args()
+
+cache_dir = Path(args.cache_dir)
+cache_dir.mkdir(parents=True, exist_ok=True)
+
+output_dir = Path(args.output_dir)
+output_dir.mkdir(parents=True,exist_ok=True)
+
+print("Downloading Shape Predictor")
+f=open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx", cache_dir=cache_dir, return_path=True)
+predictor = dlib.shape_predictor(f)
+
+for im in Path(args.input_dir).glob("*.*"):
+    faces = align_face(str(im))
+
+    for i,face in enumerate(faces):
+        if(args.output_size):
+            factor = 1024//args.output_size
+            assert args.output_size*factor == 1024
+            D = BicubicDownSample(factor=factor)
+            face_tensor = torchvision.transforms.ToTensor()(face).unsqueeze(0).cuda()
+            face_tensor_lr = D(face_tensor)[0].cpu().detach().clamp(0, 1)
+            face = torchvision.transforms.ToPILImage()(face_tensor_lr)
+
+        face.save(Path(args.output_dir) / (im.stem+f"_{i}.png"))

drive.py

@@ -27,7 +27,7 @@ def is_url(obj: Any) -> bool:
     return True
 
 
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True) -> Any:
+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
@@ -37,7 +37,10 @@ def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: b
     if cache_dir is not None:
         cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
         if len(cache_files) == 1:
-            return open(cache_files[0], "rb")
+            if(return_path):
+                return cache_files[0]
+            else:
+                return open(cache_files[0], "rb")
 
     # Download.
     url_name = None
@@ -85,6 +88,7 @@ def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: b
         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)

run.py

@@ -7,31 +7,6 @@
 from math import log10, ceil
 import argparse
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='PULSE')
-
-    #I/O arguments
-    parser.add_argument('-input_dir', type=str, default='input', help='input data directory')
-    parser.add_argument('-output_dir', type=str, default='runs', help='output data directory')
-    parser.add_argument('-cache_dir', type=str, default='cache', help='cache directory for model weights')
-    parser.add_argument('-duplicates', type=int, default=1, help='How many HR images to produce for every image in the input directory')
-
-    #PULSE arguments
-    parser.add_argument('-seed', type=int, help='manual seed to use')
-    parser.add_argument('-loss_str', type=str, default="100*L2+0.05*GEOCROSS", help='Loss function to use')
-    parser.add_argument('-eps', type=float, default=1e-3, help='Target for downscaling loss (L2)')
-    parser.add_argument('-noise_type', type=str, default='trainable', help='zero, fixed, or trainable')
-    parser.add_argument('-num_trainable_noise_layers', type=int, default=5, help='Number of noise layers to optimize')
-    parser.add_argument('-tile_latent', action='store_true', help='Whether to forcibly tile the same latent 18 times')
-    parser.add_argument('-bad_noise_layers', type=str, default="17", help='List of noise layers to zero out to improve image quality')
-    parser.add_argument('-opt_name', type=str, default='adam', help='Optimizer to use in projected gradient descent')
-    parser.add_argument('-learning_rate', type=float, default=0.4, help='Learning rate to use during optimization')
-    parser.add_argument('-steps', type=int, default=100, help='Number of optimization steps')
-    parser.add_argument('-lr_schedule', type=str, default='linear1cycledrop', help='fixed, linear1cycledrop, linear1cycle')
-    parser.add_argument('-save_intermediate', action='store_true', help='Whether to store and save intermediate HR and LR images during optimization')
-
-    kwargs = vars(parser.parse_args())
-
 class Images(Dataset):
     def __init__(self, root_dir, duplicates):
         self.root_path = Path(root_dir)
@@ -49,6 +24,30 @@ def __getitem__(self, idx):
         else:
             return image,img_path.stem+f"_{(idx % self.duplicates)+1}"
 
+parser = argparse.ArgumentParser(description='PULSE')
+
+#I/O arguments
+parser.add_argument('-input_dir', type=str, default='input', help='input data directory')
+parser.add_argument('-output_dir', type=str, default='runs', help='output data directory')
+parser.add_argument('-cache_dir', type=str, default='cache', help='cache directory for model weights')
+parser.add_argument('-duplicates', type=int, default=1, help='How many HR images to produce for every image in the input directory')
+
+#PULSE arguments
+parser.add_argument('-seed', type=int, help='manual seed to use')
+parser.add_argument('-loss_str', type=str, default="100*L2+0.05*GEOCROSS", help='Loss function to use')
+parser.add_argument('-eps', type=float, default=1e-3, help='Target for downscaling loss (L2)')
+parser.add_argument('-noise_type', type=str, default='trainable', help='zero, fixed, or trainable')
+parser.add_argument('-num_trainable_noise_layers', type=int, default=5, help='Number of noise layers to optimize')
+parser.add_argument('-tile_latent', action='store_true', help='Whether to forcibly tile the same latent 18 times')
+parser.add_argument('-bad_noise_layers', type=str, default="17", help='List of noise layers to zero out to improve image quality')
+parser.add_argument('-opt_name', type=str, default='adam', help='Optimizer to use in projected gradient descent')
+parser.add_argument('-learning_rate', type=float, default=0.4, help='Learning rate to use during optimization')
+parser.add_argument('-steps', type=int, default=100, help='Number of optimization steps')
+parser.add_argument('-lr_schedule', type=str, default='linear1cycledrop', help='fixed, linear1cycledrop, linear1cycle')
+parser.add_argument('-save_intermediate', action='store_true', help='Whether to store and save intermediate HR and LR images during optimization')
+
+kwargs = vars(parser.parse_args())
+
 dataset = Images(kwargs["input_dir"], duplicates=kwargs["duplicates"])
 out_path = Path(kwargs["output_dir"])
 out_path.mkdir(parents=True, exist_ok=True)