extract-1adrianb-crop-face-alignment

face-crop-extractor.py

# for use with https://github.com/1adrianb/face-alignment
import cv2
import numpy as np
import face_alignment
imread = cv2.imread
_DEAFAULT_JPG_QUALITY = 99
imwrite = partial(cv2.imwrite, params=[int(cv2.IMWRITE_JPEG_QUALITY), _DEAFAULT_JPG_QUALITY])


fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cuda')
# fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, face_detector='dlib')

def	cutout(ipath):
	global fa
	if isinstance(ipath, str):
		original255 = skimage.io.imread(ipath).astype(np.ubyte)
	else:
		original255 = ipath.copy()
		ipath = '_'
	# original255 = imread(ipath)

	# print(original255.shape)
	if original255.shape[-1] == 4:
		original255 = original255[:, :, :3]

	frame = original255


	print('frame',frame)

	# Run the face alignment tracker on the webcam image
	imagePoints = fa.get_landmarks_from_image(frame)
	
	print('')
	print('imagePoints',imagePoints)
	print('')

	chipSize = 512
	chipCorners = np.float32([[0,0],
	                          [chipSize,0],
	                          [0,chipSize],
	                          [chipSize,chipSize]])

	
	if(imagePoints is not None):
		print('processing points')

		imagePoints = imagePoints[0]

		# Compute the Anchor Landmarks
		# This ensures the eyes and chin will not move within the chip
		rightEyeMean = np.mean(imagePoints[36:42], axis=0)
		leftEyeMean  = np.mean(imagePoints[42:47], axis=0)
		middleEye    = (rightEyeMean + leftEyeMean) * 0.5
		chin         = imagePoints[8]
		#cv2.circle(frame, tuple(rightEyeMean[:2].astype(int)), 30, (255,255,0))
		#cv2.circle(frame, tuple(leftEyeMean [:2].astype(int)), 30, (255,0,255))

		# Compute the chip center and up/side vectors
		mean = middleEye[:2] #((middleEye * 3) + chin) * 0.25
		# centered = imagePoints - mean 

		upVector    = (chin        - middleEye)[:2] * 1.2

		rightVector	=	np.array([ upVector[1], -upVector[0] ])

		# Compute the corners of the facial chip
		imageCorners = np.float32([(mean + ((-rightVector - upVector))),
		                           (mean + (( rightVector - upVector))),
		                           (mean + ((-rightVector + upVector))),
		                           (mean + (( rightVector + upVector)))])

		# Compute the Perspective Homography and Extract the chip from the image
		chipMatrix = cv2.getPerspectiveTransform(imageCorners, chipCorners)
		chip = cv2.warpPerspective(frame, chipMatrix, (chipSize, chipSize))
	return	chip


img  = imread(img_path)
crop = cutout(img)
imwrite(new_img_path, crop)