face.py
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import cv2
import os
import shutil
Img_dir = '../cacheUserImg'
Copy_dir = '../cacheUserImgCopy'
cv2.namedWindow("show", 0)
# 获取人脸识别训练数据
# face_cascade = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
face_cascade = cv2.CascadeClassifier('./haarcascade_frontalface_alt.xml')
def show(img, ms=0, reduce=3):
""" 显示 """
cv2.imshow('show', img)
h, w = img.shape[:2]
cv2.resizeWindow("show", w//reduce, h//reduce)
cv2.waitKey(ms)
def rotate_img_bad(img, angle):
""" 旋转 """
# 原图的高、宽 以及通道数
h, w = img.shape[:2]
M = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1.0)
rotated_img = cv2.warpAffine(img, M, (w, h))
# show(rotated_img)
cv2.imwrite(f'./test_{angle}.png', rotated_img)
return rotated_img
def rotate_img(image, angle):
# grab the dimensions of the image and then determine the
# center
(h, w) = image.shape[:2]
(cX, cY) = (w // 2, h // 2)
# grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
# print(M, type(M))
cos = abs(M[0, 0])
sin = abs(M[0, 1])
# print(f"cos {cos}, sin {sin}.\t h,w:{h},{w}\tangle:{angle} \t point:{cX},{cY}")
# compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# perform the actual rotation and return the image
rotated_img = cv2.warpAffine(image, M, (nW, nH))
# cv2.imwrite(f'./test_{angle}.png', rotated_img)
return rotated_img
def check_face(img_gray, cascade):
""" 检测人脸 """
# 探测图片中的人脸
faces = face_cascade.detectMultiScale(
img_gray,
scaleFactor=1.1, # 每次缩减比例
minNeighbors=5, # 检测多次
flags=cv2.CASCADE_SCALE_IMAGE,
minSize=(100, 100)
)
print("检测到人脸区域：{}".format(faces))
return faces
def mark_face(img, faces):
""" 标记人脸 """
print("mark face")
iy, ix = img.shape[:2]
for x, y, w, h in faces:
# cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
show(img, 100)
# 裁剪图片
print(ix, iy, x, y, w, h)
length2 = min(ix, iy)
length = int(w*2.5)
length = min(length2, length)
print(f"length: {length2} {length}")
ow = length-w
ow1 = ow//2
oh = length-h
oh1 = oh//2
y1, y2 = y-oh1, y+h+oh1
x1, x2 = x-ow1, x+w+ow1
# 检测图片溢出
print(y1, y2, x1, x2)
if y1 < 0:
print('裁剪：1 顶部溢出')
y1 = 0
y2 = length
if y2 > iy:
print('裁剪：2 底部溢出')
y2 = iy
y1 = iy-length
if x1 < 0:
print('裁剪：3 左侧溢出')
x1 = 0
x2 = length
if x2 > ix:
print('裁剪：4 右侧溢出')
x2 = ix
x1 = ix-length
img2 = img[y1:y2, x1:x2]
show(img2, 200)
return img2
def run(img_name):
# 读取图片，灰度转换
img_bgr = cv2.imread(os.path.join(Img_dir, img_name))
img_gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
# 旋转图片
find = False
for angle in range(0, 360, 90):
show(rotate_img(img_bgr, angle), 100)
print(f"angle: {angle}")
if angle > 0:
img_bgr_r = rotate_img(img_bgr, angle)
img_gray_r = rotate_img(img_gray, angle)
else:
img_bgr_r = img_bgr
img_gray_r = img_gray
faces = check_face(img_gray_r, face_cascade)
if not isinstance(faces, tuple):
print("find face")
img_final = mark_face(img_bgr_r, faces)
find = True
break
# show(img_bgr_r, 1000)
print('end')
if not find:
print("未找到人脸，使用原图")
img_final = img_bgr
cv2.imwrite(os.path.join(Img_dir, img_name), img_final)
if __name__ == '__main__':
file_list = os.listdir(Img_dir)
for i in range(len(file_list)):
file_name = file_list[i]
print("处理图片{0}/{1}: {2}".format(i+1, len(file_list), file_name))
if not os.path.exists(os.path.join(Copy_dir, file_name)):
shutil.copy(f'{Img_dir}/{file_name}', f'{Copy_dir}/{file_name}')
try:
run(file_name)
except Exception as _:
pass
cv2.destroyAllWindows()