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https://github.com/iperov/DeepFaceLab.git
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96 lines
No EOL
4.1 KiB
Python
96 lines
No EOL
4.1 KiB
Python
import traceback
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import numpy as np
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import os
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import cv2
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from pathlib import Path
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class LandmarksExtractor(object):
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def __init__ (self, keras):
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self.keras = keras
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K = self.keras.backend
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def __enter__(self):
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keras_model_path = Path(__file__).parent / "2DFAN-4.h5"
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if not keras_model_path.exists():
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return None
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self.keras_model = self.keras.models.load_model (str(keras_model_path))
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return self
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def __exit__(self, exc_type=None, exc_value=None, traceback=None):
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del self.keras_model
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return False #pass exception between __enter__ and __exit__ to outter level
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def extract_from_bgr (self, input_image, rects):
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input_image = input_image[:,:,::-1].copy()
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(h, w, ch) = input_image.shape
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landmarks = []
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for (left, top, right, bottom) in rects:
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try:
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center = np.array( [ (left + right) / 2.0, (top + bottom) / 2.0] )
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center[1] -= (bottom - top) * 0.12
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scale = (right - left + bottom - top) / 195.0
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image = self.crop(input_image, center, scale).astype(np.float32)
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image = np.expand_dims(image, 0)
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predicted = self.keras_model.predict (image).transpose (0,3,1,2)
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pts_img = self.get_pts_from_predict ( predicted[-1], center, scale)
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pts_img = [ ( int(pt[0]), int(pt[1]) ) for pt in pts_img ]
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landmarks.append ( ( (left, top, right, bottom),pts_img ) )
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except Exception as e:
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print ("extract_from_bgr: ", traceback.format_exc() )
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landmarks.append ( ( (left, top, right, bottom), [ (0,0) for _ in range(68) ] ) )
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return landmarks
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def transform(self, point, center, scale, resolution):
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pt = np.array ( [point[0], point[1], 1.0] )
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h = 200.0 * scale
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m = np.eye(3)
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m[0,0] = resolution / h
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m[1,1] = resolution / h
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m[0,2] = resolution * ( -center[0] / h + 0.5 )
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m[1,2] = resolution * ( -center[1] / h + 0.5 )
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m = np.linalg.inv(m)
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return np.matmul (m, pt)[0:2]
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def crop(self, image, center, scale, resolution=256.0):
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ul = self.transform([1, 1], center, scale, resolution).astype( np.int )
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br = self.transform([resolution, resolution], center, scale, resolution).astype( np.int )
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if image.ndim > 2:
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newDim = np.array([br[1] - ul[1], br[0] - ul[0], image.shape[2]], dtype=np.int32)
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newImg = np.zeros(newDim, dtype=np.uint8)
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else:
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newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
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newImg = np.zeros(newDim, dtype=np.uint8)
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ht = image.shape[0]
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wd = image.shape[1]
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newX = np.array([max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
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newY = np.array([max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
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oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
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oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
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newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
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newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), interpolation=cv2.INTER_LINEAR)
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return newImg
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def get_pts_from_predict(self, a, center, scale):
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b = a.reshape ( (a.shape[0], a.shape[1]*a.shape[2]) )
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c = b.argmax(1).reshape ( (a.shape[0], 1) ).repeat(2, axis=1).astype(np.float)
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c[:,0] %= a.shape[2]
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c[:,1] = np.apply_along_axis ( lambda x: np.floor(x / a.shape[2]), 0, c[:,1] )
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for i in range(a.shape[0]):
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pX, pY = int(c[i,0]), int(c[i,1])
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if pX > 0 and pX < 63 and pY > 0 and pY < 63:
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diff = np.array ( [a[i,pY,pX+1]-a[i,pY,pX-1], a[i,pY+1,pX]-a[i,pY-1,pX]] )
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c[i] += np.sign(diff)*0.25
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c += 0.5
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return [ self.transform (c[i], center, scale, a.shape[2]) for i in range(a.shape[0]) ] |