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added new extractor: S3FD,
all extractors now produce less false-positive faces
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iperov
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9440224556
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10 changed files with 83 additions and 112 deletions
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@ -3,101 +3,18 @@ import os
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import cv2
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from pathlib import Path
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def transform(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(image, center, scale, resolution=256.0):
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ul = transform([1, 1], center, scale, resolution).astype( np.int )
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br = 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(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 [ transform (c[i], center, scale, a.shape[2]) for i in range(a.shape[0]) ]
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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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class TorchBatchNorm2D(self.keras.layers.Layer):
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def __init__(self, axis=-1, momentum=0.99, epsilon=1e-3, **kwargs):
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super(TorchBatchNorm2D, self).__init__(**kwargs)
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self.supports_masking = True
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self.axis = axis
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self.momentum = momentum
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self.epsilon = epsilon
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def build(self, input_shape):
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dim = input_shape[self.axis]
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if dim is None:
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raise ValueError('Axis ' + str(self.axis) + ' of ' 'input tensor should have a defined dimension ' 'but the layer received an input with shape ' + str(input_shape) + '.')
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shape = (dim,)
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self.gamma = self.add_weight(shape=shape, name='gamma', initializer='ones', regularizer=None, constraint=None)
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self.beta = self.add_weight(shape=shape, name='beta', initializer='zeros', regularizer=None, constraint=None)
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self.moving_mean = self.add_weight(shape=shape, name='moving_mean', initializer='zeros', trainable=False)
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self.moving_variance = self.add_weight(shape=shape, name='moving_variance', initializer='ones', trainable=False)
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self.built = True
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def call(self, inputs, training=None):
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input_shape = K.int_shape(inputs)
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broadcast_shape = [1] * len(input_shape)
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broadcast_shape[self.axis] = input_shape[self.axis]
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broadcast_moving_mean = K.reshape(self.moving_mean, broadcast_shape)
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broadcast_moving_variance = K.reshape(self.moving_variance, broadcast_shape)
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broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
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broadcast_beta = K.reshape(self.beta, broadcast_shape)
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invstd = K.ones (shape=broadcast_shape, dtype='float32') / K.sqrt(broadcast_moving_variance + K.constant(self.epsilon, dtype='float32'))
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return (inputs - broadcast_moving_mean) * invstd * broadcast_gamma + broadcast_beta
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def get_config(self):
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config = { 'axis': self.axis, 'momentum': self.momentum, 'epsilon': self.epsilon }
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base_config = super(TorchBatchNorm2D, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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self.TorchBatchNorm2D = TorchBatchNorm2D
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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), custom_objects={'TorchBatchNorm2D': self.TorchBatchNorm2D} )
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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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@ -116,13 +33,58 @@ class LandmarksExtractor(object):
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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 = crop(input_image, center, scale).transpose ( (2,0,1) ).astype(np.float32) / 255.0
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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)
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pts_img = get_pts_from_predict ( predicted[-1], center, scale)
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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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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]) ]
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