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9f3fa29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | # -*- coding: utf-8 -*-
"""
Created on Thu Jun 24 11:36:01 2021
@author: Xinya
"""
import os
import glob
import time
import numpy as np
import csv
import cv2
import dlib
from skimage import transform as tf
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
import imageio
def save(path, frames, format):
if format == '.mp4':
imageio.mimsave(path, frames)
elif format == '.png':
if not os.path.exists(path):
os.makedirs(path)
for j, frame in enumerate(frames):
cv2.imwrite(path+'/'+str(j)+'.png',frame)
# imageio.imsave(os.path.join(path, str(j) + '.png'), frames[j])
else:
print ("Unknown format %s" % format)
exit()
def crop_image(image_path, out_path):
template = np.load('./M003_template.npy')
image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1) #detect human face
if len(rects) != 1:
return 0
for (j, rect) in enumerate(rects):
shape = predictor(gray, rect) #detect 68 points
shape = shape_to_np(shape)
pts2 = np.float32(template[:47,:])
# pts2 = np.float32(template[17:35,:])
# pts1 = np.vstack((landmark[27:36,:], landmark[39,:],landmark[42,:],landmark[45,:]))
pts1 = np.float32(shape[:47,:]) #eye and nose
# pts1 = np.float32(landmark[17:35,:])
tform = tf.SimilarityTransform()
tform.estimate( pts2, pts1) #Set the transformation matrix with the explicit parameters.
dst = tf.warp(image, tform, output_shape=(256, 256))
dst = np.array(dst * 255, dtype=np.uint8)
cv2.imwrite(out_path,dst)
def shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((shape.num_parts, 2), dtype=dtype)
# loop over all facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, shape.num_parts):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords
def crop_image_tem(video_path, out_path):
image_all = []
videoCapture = cv2.VideoCapture(video_path)
success, frame = videoCapture.read()
n = 0
while success :
image_all.append(frame)
n = n + 1
success, frame = videoCapture.read()
if len(image_all)!=0 :
template = np.load('./M003_template.npy')
image=image_all[0]
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1) #detect human face
if len(rects) != 1:
return 0
for (j, rect) in enumerate(rects):
shape = predictor(gray, rect) #detect 68 points
shape = shape_to_np(shape)
pts2 = np.float32(template[:47,:])
# pts2 = np.float32(template[17:35,:])
# pts1 = np.vstack((landmark[27:36,:], landmark[39,:],landmark[42,:],landmark[45,:]))
pts1 = np.float32(shape[:47,:]) #eye and nose
# pts1 = np.float32(landmark[17:35,:])
tform = tf.SimilarityTransform()
tform.estimate( pts2, pts1) #Set the transformation matrix with the explicit parameters.
out = []
for i in range(len(image_all)):
image = image_all[i]
dst = tf.warp(image, tform, output_shape=(256, 256))
dst = np.array(dst * 255, dtype=np.uint8)
out.append(dst)
if not os.path.exists(out_path):
os.makedirs(out_path)
save(out_path,out,'.png')
def proc_audio(src_mouth_path, dst_audio_path):
audio_command = 'ffmpeg -i \"{}\" -loglevel error -y -f wav -acodec pcm_s16le ' \
'-ar 16000 \"{}\"'.format(src_mouth_path, dst_audio_path)
os.system(audio_command)
if __name__ == "__main__":
#video alignment
video_path = './test/crop/M030_sad_3_001.mp4'
out_path = './test/crop/M030_sad_3_001'
crop_image_tem(video_path, out_path)
#image alignment
image_path = './test/raw_image/brade2.jpg'
out_path = './test/image/brade2.jpg'
crop_image(image_path, out_path)
#change_audio_sample_rate
src_mouth_path = './test/audio/00015.mp3'
dst_audio_path = './test/audio/00015.mov'
proc_audio(src_mouth_path, dst_audio_path)
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