Create app.py

app.py

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+import os
+os.system("git clone https://github.com/bryandlee/animegan2-pytorch")
+
+os.system("gdown https://drive.google.com/uc?id=1WK5Mdt6mwlcsqCZMHkCUSDJxN1UyFi0-")
+os.system("gdown https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU")
+
+import sys
+sys.path.append("animegan2-pytorch")
+
+import torch
+torch.set_grad_enabled(False)
+
+from model import Generator
+
+device = "cpu"
+
+model = Generator().eval().to(device)
+model.load_state_dict(torch.load("face_paint_512_v2_0.pt"))
+
+from PIL import Image
+from torchvision.transforms.functional import to_tensor, to_pil_image
+
+def face2paint(
+    img: Image.Image,
+    size: int,
+    side_by_side: bool = True,
+) -> Image.Image:
+
+    w, h = img.size
+    s = min(w, h)
+    img = img.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
+    img = img.resize((size, size), Image.LANCZOS)
+
+    input = to_tensor(img).unsqueeze(0) * 2 - 1
+    output = model(input.to(device)).cpu()[0]
+
+    if side_by_side:
+        output = torch.cat([input[0], output], dim=2)
+
+    output = (output * 0.5 + 0.5).clip(0, 1)
+
+    return to_pil_image(output)
+    
+
+#@title Face Detector & FFHQ-style Alignment
+
+# https://github.com/woctezuma/stylegan2-projecting-images
+
+import os
+import dlib
+import collections
+from typing import Union, List
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+
+
+def get_dlib_face_detector(predictor_path: str = "shape_predictor_68_face_landmarks.dat"):
+
+    if not os.path.isfile(predictor_path):
+        model_file = "shape_predictor_68_face_landmarks.dat.bz2"
+        os.system(f"wget http://dlib.net/files/{model_file}")
+        os.system(f"bzip2 -dk {model_file}")
+
+    detector = dlib.get_frontal_face_detector()
+    shape_predictor = dlib.shape_predictor(predictor_path)
+
+    def detect_face_landmarks(img: Union[Image.Image, np.ndarray]):
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        faces = []
+        dets = detector(img)
+        for d in dets:
+            shape = shape_predictor(img, d)
+            faces.append(np.array([[v.x, v.y] for v in shape.parts()]))
+        return faces
+    
+    return detect_face_landmarks
+
+
+def display_facial_landmarks(
+    img: Image, 
+    landmarks: List[np.ndarray],
+    fig_size=[15, 15]
+):
+    plot_style = dict(
+        marker='o',
+        markersize=4,
+        linestyle='-',
+        lw=2
+    )
+    pred_type = collections.namedtuple('prediction_type', ['slice', 'color'])
+    pred_types = {
+        'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)),
+        'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)),
+        'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)),
+        'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)),
+        'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)),
+        'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)),
+        'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)),
+        'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)),
+        'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4))
+    }
+
+    fig = plt.figure(figsize=fig_size)
+    ax = fig.add_subplot(1, 1, 1)
+    ax.imshow(img)
+    ax.axis('off')
+
+    for face in landmarks:
+        for pred_type in pred_types.values():
+            ax.plot(
+                face[pred_type.slice, 0],
+                face[pred_type.slice, 1],
+                color=pred_type.color, **plot_style
+            )
+    plt.show()
+
+import PIL.Image
+import PIL.ImageFile
+import numpy as np
+import scipy.ndimage
+
+
+def align_and_crop_face(
+    img: Image.Image,
+    landmarks: np.ndarray,
+    expand: float = 1.0,
+    output_size: int = 1024, 
+    transform_size: int = 4096,
+    enable_padding: bool = True,
+):
+    # Parse landmarks.
+    # pylint: disable=unused-variable
+    lm = landmarks
+    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)
+    x *= expand
+    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
+
+    # 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
+    
+
+import requests
+
+def inference(image):
+  img = image
+  face_detector = get_dlib_face_detector()
+  landmarks = face_detector(img)
+  
+  display_facial_landmarks(img, landmarks, fig_size=[5, 5])
+  
+  for landmark in landmarks:
+      face = align_and_crop_face(img, landmark, expand=1.3)
+      out = face2paint(face, 512)
+  
+  return out
+  
+  
+
+iface = gr.Interface(inference, "image", "image")
+iface.launch()