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!pip install dlib
!pip install imutils
!pip install opencv-python
!pip install libopencv
# coding:utf-8
import dlib
from imutils import face_utils
import cv2
from google.colab.patches import cv2_imshow
from scipy.ndimage import gaussian_filter
import skimage.transform
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import IPython
#事前に画像を400×400に加工しておく
#加工済みの画像を使用する
#画像の呼び込み
img = cv2.imread('/content/drive/MyDrive/知能システムプログラミング通論/ayase.jpg', cv2.IMREAD_UNCHANGED)
#ここから顔検出
#すっぴん側のランドマーク検出
# 顔検出ツールの呼び出し
face_detector = dlib.get_frontal_face_detector()
# 顔のランドマーク検出ツールの呼び出し
predictor_path = '/content/drive/MyDrive/知能システムプログラミング通論/shape_predictor_68_face_landmarks.dat'
face_predictor = dlib.shape_predictor(predictor_path)
# 処理高速化のためグレースケール化
img_gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 顔のランドマーク検出
# ※2番めの引数はupsampleの回数
faces = face_detector(img_gry,1)
# 検出した全顔に対して処理
for face in faces:
# 顔のランドマーク検出
landmark = face_predictor(img_gry, face)
# 処理高速化のためランドマーク群をNumPy配列に変換(必須)
landmark = face_utils.shape_to_np(landmark)
#メイク側のランドマーク検出
imgMake=cv2.imread('/content/drive/MyDrive/知能システムプログラミング通論/green.jpg',cv2.IMREAD_UNCHANGED)
cv2_imshow(img)
cv2_imshow(imgMake)
predictor_path = '/content/drive/MyDrive/知能システムプログラミング通論/shape_predictor_68_face_landmarks.dat'
face_predictor2 = dlib.shape_predictor(predictor_path)
img_gry2 = cv2.cvtColor(imgMake, cv2.COLOR_BGR2GRAY)
faces = face_detector(img_gry2,1)
for face in faces:
# 顔のランドマーク検出
landmark2 = face_predictor2(img_gry2, face)
# 処理高速化のためランドマーク群をNumPy配列に変換(必須)
landmark2 = face_utils.shape_to_np(landmark2)
#print(landmark2)
#ここからアフィン変換
#アフィン変換の巻数
def distort(file_path, roi_points, from_points, to_points):
im = Image.open(file_path).convert('RGBA')
from_points = np.concatenate((roi_points, from_points)) #すっぴんのランドマーク
to_points = np.concatenate((roi_points, to_points)) #化粧のランドマーク
affin = skimage.transform.PiecewiseAffineTransform() #区分的アフィン変換
affin.estimate(to_points, from_points)
im_array = skimage.transform.warp(im, affin)
im_array = np.array(im_array * 255., dtype=np.uint8)
if im_array.shape[2] == 1:
im_array = im_array.reshape((im_array.shape[0],im_array.shape[1]))
warped_im = Image.fromarray(im_array, 'RGBA') #PIL.Imageが得られる
im.paste(warped_im, (0, 0), warped_im)
return im
file_path = '/content/drive/MyDrive/知能システムプログラミング通論/green.jpg'
roi_points = [(0,0),(400,0),(400,400),(0,400)]
from_points = [
landmark2[0],landmark2[1],landmark2[2],landmark2[3],landmark2[4],landmark2[5],
landmark2[6],landmark2[7], landmark2[8],landmark2[9],landmark2[10],
landmark2[11],landmark2[12],landmark2[13],landmark2[14],landmark2[15],
landmark2[16],landmark2[27],landmark2[28],landmark2[29],landmark2[30],
landmark2[31] ,landmark2[32],landmark2[33],landmark2[34],landmark2[35],
landmark2[36] ,landmark2[37],landmark2[38],landmark2[39],landmark2[40],
landmark2[41],landmark2[42],landmark2[43],landmark2[44],landmark2[45],
landmark2[46],landmark2[47], landmark2[48],landmark2[49],landmark2[50],
landmark2[51],landmark2[52],landmark2[53],landmark2[54],landmark2[55],
landmark2[56],landmark2[57],landmark2[58],landmark2[59],landmark2[60],
landmark2[61],landmark2[62],landmark2[63],landmark2[64],landmark2[65],
landmark2[66],landmark2[67]]
to_points = [
landmark[0],landmark[1],landmark[2],landmark[3],landmark[4],landmark[5],
landmark[6],landmark[7], landmark[8],landmark[9],landmark[10],
landmark[11],landmark[12],landmark[13],landmark[14],landmark[15],
landmark[16],landmark[27],landmark[28],landmark[29],landmark[30],
landmark[31] ,landmark[32],landmark[33],landmark[34],landmark[35],
landmark[36] ,landmark[37],landmark[38],landmark[39],landmark[40],
landmark[41],landmark[42],landmark[43],landmark[44],landmark[45],
landmark[46],landmark[47], landmark[48],landmark[49],landmark[50],
landmark[51],landmark[52],landmark[53],landmark[54],landmark[55],
landmark[56],landmark[57],landmark[58],landmark[59],landmark[60],
landmark[61],landmark[62],landmark[63],landmark[64],landmark[65],
landmark[66],landmark[67]]
#print(from_points)
#print(to_points)
im = distort(file_path, roi_points, from_points, to_points)
im.save('/content/drive/MyDrive/知能システムプログラミング通論/affine.png') #アフィン変換後の画像を保存
img_for_filter=cv2.imread('/content/drive/MyDrive/知能システムプログラミング通論/affine.png')
lab=cv2.cvtColor(img_for_filter, cv2.COLOR_BGR2LAB)
l,a,b=cv2.split(lab)
'''
cv2_imshow(l)
cv2_imshow(a)
cv2_imshow(b)
'''
#cv2.imwrite('/content/drive/MyDrive/知能システムプログラミング通論/reSize.jpg',im)
#trim_img.save('./image_dir/img_'+str(count)+'_trim.jpg')
#img.save('/content/drive/MyDrive/知能システムプログラミング通論/lena.jpg')
#
# --------------------------------
# 3.結果表示
# --------------------------------
#cv2.imread('sample', cv2.IMREAD_UNCHANGED)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
# 結果を出力
#cv2.imwrite("/content/drive/MyDrive/知能システムプログラミング通論/output.png", dst)
#l=明るさ、輝度値 s=構造、structure
'''
# ランドマーク描画
displayNum = 0 # 描画用の数字
'''
'''
for (i, (x, y)) in enumerate(landmark):
cv2.circle(img, (x, y), 1, (255, 255, 0), -1)
if displayNum < 60:
cv2.putText(img, str(displayNum), (x-3,y-3), cv2.FONT_HERSHEY_SIMPLEX, 0.2, (255,255,255), 1, cv2.LINE_AA)
displayNum += 1
'''
mask = np.zeros_like(img)
background = np.zeros_like(img)
cv2.fillPoly(mask, [np.concatenate([landmark[0:17],np.flipud(landmark[17:27])])], color=(255, 255, 255))
cv2.fillPoly(mask, [landmark[36:42]], color=(0, 0, 0))
cv2.fillPoly(mask, [landmark[42:48]], color=(0, 0, 0))
cv2.fillPoly(mask, [landmark[48:60]], color=(0, 0, 0))
mask2 = np.zeros_like(img)
cv2.fillPoly(mask2, [np.concatenate([landmark[0:17],np.flipud(landmark[17:27])])], color=(255, 255, 255))
cv2.fillPoly(mask2, [landmark[36:42]], color=(0, 0, 0))
cv2.fillPoly(mask2, [landmark[42:48]], color=(0, 0, 0))
w = img.shape[1]
h = img.shape[0]
bi = cv2.bilateralFilter(img, 15, 20, 20)
bilateraled = np.where(mask[:h, :w] == 0, img , bi)
lab_input = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
input_l,input_a,input_b = cv2.split(lab_input)
lab_bilateraled = cv2.cvtColor(bilateraled, cv2.COLOR_BGR2LAB)
bilateraled_l,bilateraled_a,bilateraled_b = cv2.split(lab_bilateraled)
'''
im_diff = input_l.astype(int)- bilateraled_l.astype(int)
im_diff_abs = np.abs(im_diff)
im_diff_abs_norm = im_diff_abs / im_diff_abs.max() * 255
im_diff_abs_norm = im_diff_abs_norm.astype(int)
#cv2_imshow(im_diff_abs_norm)
cv2.imwrite('/content/drive/MyDrive/知能システムプログラミング通論/before.png',im_diff_abs_norm)
print("************")
'''
imMake=cv2.imread('/content/drive/MyDrive/知能システムプログラミング通論/affine.png')
#cv2_imshow(imMake)
w = imMake.shape[1]
h = imMake.shape[0]
imMake_lab = cv2.cvtColor(imMake, cv2.COLOR_BGR2LAB)
imMake_l,imMake_a,imMake_b = cv2.split(imMake_lab)
bi_imMake = cv2.bilateralFilter(imMake, 15, 20, 20)
warped = np.where(mask[:h, :w] == 0, imMake , bi_imMake)
warped_lab = cv2.cvtColor(bi_imMake, cv2.COLOR_BGR2LAB)
warped_l,warped_a,warped_b = cv2.split(warped_lab)
im_diff = imMake.astype(int) - warped.astype(int)
im_diff_l,im_diff_a,im_diff_b=cv2.split(im_diff)
im_diff_abs = np.abs(im_diff)
im_diff_abs_norm = im_diff_abs / im_diff_abs.max() * 255
im_diff_abs_norm = im_diff_abs_norm.astype(int)
#cv2_imshow(im_diff_abs_norm)
cv2.imwrite('/content/drive/MyDrive/知能システムプログラミング通論/kate.png',im_diff_abs_norm)
"""
warped = np.where(mask[:h, :w] == 0, background , bi_imMake)
gaussian = cv2.GaussianBlur(warped, (9, 9),5)
blended= cv2.addWeighted(img, 1, gaussian, 0.1, 1)
"""
#blended = np.where(mask2[:h, :w] == 0, img , blended)
#blended_lab = cv2.cvtColor(blended, cv2.COLOR_BGR2LAB)
#blended_l,blended_a,blended_b = cv2.split(blended_lab)
#cv2_imshow(blended)
result_l = im_diff_l + bilateraled_l
"""
diff_ary = np.array(result_l , np.uint8)
gaussian = cv2.GaussianBlur(diff_ary , (5, 5),9)
result_l = gaussian
"""
v = 0.2
result_a = (bilateraled_a*v)+((1-v)*warped_a)
result_b = (bilateraled_b*v)+((1-v)*warped_b)
'''
cv2_imshow(result_l)
cv2_imshow(result_a)
cv2_imshow(result_b)
'''
result = cv2.merge([result_l.astype(int),result_a.astype(int),result_b.astype(int)])
"""
lab=cv2.merge([result_l,result_a.astype(int),result_b.astype(int)])
cv2_imshow(lab)
print(type(lab))
print(result_l,result_a.astype(int),result_b.astype(int))
print(lab)
"""
img_Lab = np.array(result, np.uint8)
img_BGR = cv2.cvtColor(img_Lab, cv2.COLOR_Lab2BGR)
show = np.where(mask2[:h, :w] == 0, img,img_BGR)
#gaussian = cv2.GaussianBlur(show, (3, 3),0)
cv2_imshow(show)
#end = color.lab2rgb(result)
cv2.imwrite('/content/drive/MyDrive/知能システムプログラミング通論/result_c.png',show)
#cv2.imwrite('/content/drive/MyDrive/知能システムプログラミング通論/lab.png',lab)
#cv2_imshow(a)
#cv2.polylines(img, [np.concatenate([landmark[0:17],np.flipud(landmark[17:27])])], True, (255, 255, 255), thickness=2)
# --------------------------------
# 3.結果表示
# --------------------------------
#cv2.imread('sample', cv2.IMREAD_UNCHANGED)
#cv2_imshow(output)
#cv2.waitKey(0)
#cv2.destroyAllWindows()