人臉圖像切割分離工具

最近在做人臉識別，需要對人臉數據集進行處理，對一張或批量圖像的人臉進行切割分離，並且另保存下來。受網上開源工具的啓發，在借鑑他人的基礎上進行了改進，使得更加方便實用。

以下代碼是改進版，分爲兩部分功能：

1. 一張人臉圖片切割並顯示，不保存
2. 一張/批量人臉圖像切割並保存

在這裏需要申明一下，尊重原著！（轉發需要標註一下原著信息）

原創版：

  Author:    coneypo
  Blog:       http://www.cnblogs.com/AdaminXie
  GitHub:   https://github.com/coneypo/Dlib_face_cut

改進版：

Improver:  Cai_90hou

Blog:         https://blog.csdn.net/qq_38677310/article/details/84702662

Github:      

#crop_faces_show.py

import dlib         # 人臉識別的庫dlib
import numpy as np  # 數據處理的庫numpy
import cv2          # 圖像處理的庫OpenCv

# Dlib 正向人臉檢測器
detector = dlib.get_frontal_face_detector()

# 讀取圖像
path = "faces_for_test/"

img = cv2.imread(path+"test_faces_1.jpg")


# Dlib 檢測
dets = detector(img, 1)

print("檢測到的人臉數 / faces ：", len(dets), "\n")

# 記錄人臉矩陣大小
height_max = 0
width_sum = 0

# 計算要生成的圖像 img_blank 大小
for k, d in enumerate(dets):

    # 計算矩形大小
    # (x,y), (寬度width, 高度height)
    pos_start = tuple([d.left(), d.top()])
    pos_end = tuple([d.right(), d.bottom()])

    # 計算矩形框大小
    height = d.bottom()-d.top()
    width = d.right()-d.left()

    # 處理寬度
    width_sum += width

    # 處理高度
    if height > height_max:
        height_max = height
    else:
        height_max = height_max

# 繪製用來顯示人臉的圖像的大小
print("窗口大小："
      , '\n', "高度 / height :", height_max
      , '\n', "寬度 / width : ", width_sum)

# 生成用來顯示的圖像
img_blank = np.zeros((height_max, width_sum, 3), np.uint8)

# 記錄每次開始寫入人臉像素的寬度位置
blank_start = 0

# 將人臉填充到img_blank
for k, d in enumerate(dets):

    height = d.bottom()-d.top()
    width = d.right()-d.left()

    # 填充
    for i in range(height):
        for j in range(width):
                img_blank[i][blank_start+j] = img[d.top()+i][d.left()+j]
    # 調整圖像
    blank_start += width

cv2.namedWindow("img_faces", 0)
cv2.imshow("img_faces", img_blank)
cv2.waitKey(0)

#crop_faces_save.py

import dlib         # 人臉識別的庫dlib
import numpy as np  # 數據處理的庫numpy
import cv2          # 圖像處理的庫OpenCv
import os

#切割後的人臉序號，爲全局變量
image_num = 0

# Dlib 正向人臉檢測器
detector = dlib.get_frontal_face_detector()

# 讀取圖像的路徑
path_read = "faces_for_test/"
#img = cv2.imread(path_read+"test_faces_3.jpg")

# 用來存儲生成的單張人臉的路徑
path_save = "faces_separated/"

#將文件夾中待處理的圖片存於列表中
imgs_read  = os.listdir(path_read)   #源地址文件夾
imgs_write = os.listdir(path_save)   #存儲地址文件夾

# Delete old images
def clear_images():
    for img in imgs_write:
        os.remove(path_save + img)
    print("clean finish", '\n')

#處理一張圖片：
def cut_one_photo(img):
    global image_num

    # Dlib 檢測
    faces = detector(img, 1)

    print("人臉數：", len(faces))

    for k, d in enumerate(faces):

    # 計算矩形大小
    # (x,y), (寬度width, 高度height)
        pos_start = tuple([d.left(), d.top()])
        pos_end   = tuple([d.right(), d.bottom()])

        # 計算矩形框大小
        height = d.bottom()-d.top()
        width  = d.right()-d.left()

        # 根據人臉大小生成空的圖像
        img_blank = np.zeros((height, width, 3), np.uint8)

        #複製人臉
        for i in range(height):
            for j in range(width):
                img_blank[i][j] = img[d.top()+i][d.left()+j]

        # cv2.imshow("face_"+str(k+1), img_blank)

        # 保存在本地
        #print("Save to:", path_save+"CWH"+str(image_num+1)+".jpg")
        print("Save to:",      "img_face" + str(image_num + 1) + ".jpg")
        cv2.imwrite(path_save + "img_face" + str(image_num + 1) + ".jpg", img_blank)
        image_num += 1

#批量處理圖片
def cut_batch_photoes():
    clear_images() #清除原有圖片
    for img in imgs_read:
        try:
            path = os.path.join(path_read, img)
            image = cv2.imread(path)
            cut_one_photo(image)
            print('\n')
        except:
            continue

    print('Successful operation!\n')
    print('total number of faces: ', image_num)

def main():
    # 批量處理圖片並保存
    cut_batch_photoes()

if __name__ == '__main__':
    main()