Update app.py

app.py

@@ -47,120 +47,17 @@ def face2paint(
 
 
 import os
-import dlib
+#import dlib
 import collections
 from typing import Union, List
 import numpy as np
 from PIL import Image
 
 
-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("wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
-        os.system("bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2")
-
-    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
-
-
 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