mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
226 lines
8.2 KiB
Python
226 lines
8.2 KiB
Python
import os
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import traceback
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from pathlib import Path
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import cv2
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import numpy as np
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from facelib import FaceType, LandmarksProcessor
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from nnlib import nnlib
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"""
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ported from https://github.com/1adrianb/face-alignment
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"""
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class FANExtractor(object):
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def __init__ (self):
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pass
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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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exec( nnlib.import_all(), locals(), globals() )
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self.model = FANExtractor.BuildModel()
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self.model.load_weights(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.model
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return False #pass exception between __enter__ and __exit__ to outter level
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def extract (self, input_image, rects, second_pass_extractor=None, is_bgr=True, multi_sample=False):
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if len(rects) == 0:
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return []
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if is_bgr:
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input_image = input_image[:,:,::-1]
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is_bgr = False
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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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scale = (right - left + bottom - top) / 195.0
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center = np.array( [ (left + right) / 2.0, (top + bottom) / 2.0] )
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centers = [ center ]
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if multi_sample:
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centers += [ center + [-1,-1],
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center + [1,-1],
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center + [1,1],
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center + [-1,1],
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]
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images = []
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ptss = []
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try:
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for c in centers:
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images += [ self.crop(input_image, c, scale) ]
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images = np.stack (images)
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predicted = self.model.predict (images.astype(np.float32) / 255.0).transpose (0,3,1,2)
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for i, pred in enumerate(predicted):
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ptss += [ self.get_pts_from_predict ( pred, centers[i], scale) ]
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pts_img = np.mean ( np.array(ptss), 0 )
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landmarks.append (pts_img)
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except:
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landmarks.append (None)
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if second_pass_extractor is not None:
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for i in range(len(landmarks)):
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try:
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lmrks = landmarks[i]
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if lmrks is None:
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continue
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image_to_face_mat = LandmarksProcessor.get_transform_mat (lmrks, 256, FaceType.FULL)
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face_image = cv2.warpAffine(input_image, image_to_face_mat, (256, 256), cv2.INTER_CUBIC )
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rects2 = second_pass_extractor.extract(face_image, is_bgr=is_bgr)
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if len(rects2) != 1: #dont do second pass if faces != 1 detected in cropped image
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continue
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lmrks2 = self.extract (face_image, [ rects2[0] ], is_bgr=is_bgr, multi_sample=True)[0]
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source_lmrks2 = LandmarksProcessor.transform_points (lmrks2, image_to_face_mat, True)
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landmarks[i] = source_lmrks2
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except:
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continue
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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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a_ch, a_h, a_w = a.shape
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b = a.reshape ( (a_ch, a_h*a_w) )
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c = b.argmax(1).reshape ( (a_ch, 1) ).repeat(2, axis=1).astype(np.float)
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c[:,0] %= a_w
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c[:,1] = np.apply_along_axis ( lambda x: np.floor(x / a_w), 0, c[:,1] )
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for i in range(a_ch):
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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 np.array( [ self.transform (c[i], center, scale, a_w) for i in range(a_ch) ] )
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@staticmethod
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def BuildModel():
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def ConvBlock(out_planes, input):
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in_planes = K.int_shape(input)[-1]
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x = input
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x = BatchNormalization(momentum=0.1, epsilon=1e-05)(x)
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x = ReLU() (x)
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x = out1 = Conv2D( int(out_planes/2), kernel_size=3, strides=1, padding='valid', use_bias = False) (ZeroPadding2D(1)(x))
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x = BatchNormalization(momentum=0.1, epsilon=1e-05)(x)
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x = ReLU() (x)
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x = out2 = Conv2D( int(out_planes/4), kernel_size=3, strides=1, padding='valid', use_bias = False) (ZeroPadding2D(1)(x))
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x = BatchNormalization(momentum=0.1, epsilon=1e-05)(x)
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x = ReLU() (x)
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x = out3 = Conv2D( int(out_planes/4), kernel_size=3, strides=1, padding='valid', use_bias = False) (ZeroPadding2D(1)(x))
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x = Concatenate()([out1, out2, out3])
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if in_planes != out_planes:
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downsample = BatchNormalization(momentum=0.1, epsilon=1e-05)(input)
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downsample = ReLU() (downsample)
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downsample = Conv2D( out_planes, kernel_size=1, strides=1, padding='valid', use_bias = False) (downsample)
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x = Add ()([x, downsample])
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else:
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x = Add ()([x, input])
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return x
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def HourGlass (depth, input):
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up1 = ConvBlock(256, input)
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low1 = AveragePooling2D (pool_size=2, strides=2, padding='valid' )(input)
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low1 = ConvBlock (256, low1)
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if depth > 1:
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low2 = HourGlass (depth-1, low1)
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else:
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low2 = ConvBlock(256, low1)
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low3 = ConvBlock(256, low2)
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up2 = UpSampling2D(size=2) (low3)
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return Add() ( [up1, up2] )
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FAN_Input = Input ( (256, 256, 3) )
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x = FAN_Input
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x = Conv2D (64, kernel_size=7, strides=2, padding='valid')(ZeroPadding2D(3)(x))
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x = BatchNormalization(momentum=0.1, epsilon=1e-05)(x)
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x = ReLU()(x)
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x = ConvBlock (128, x)
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x = AveragePooling2D (pool_size=2, strides=2, padding='valid') (x)
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x = ConvBlock (128, x)
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x = ConvBlock (256, x)
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outputs = []
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previous = x
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for i in range(4):
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ll = HourGlass (4, previous)
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ll = ConvBlock (256, ll)
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ll = Conv2D(256, kernel_size=1, strides=1, padding='valid') (ll)
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ll = BatchNormalization(momentum=0.1, epsilon=1e-05)(ll)
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ll = ReLU() (ll)
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tmp_out = Conv2D(68, kernel_size=1, strides=1, padding='valid') (ll)
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outputs.append(tmp_out)
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if i < 4 - 1:
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ll = Conv2D(256, kernel_size=1, strides=1, padding='valid') (ll)
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previous = Add() ( [previous, ll, KL.Conv2D(256, kernel_size=1, strides=1, padding='valid') (tmp_out) ] )
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return Model(FAN_Input, outputs[-1] )
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