我正在尝试从本身提取的脸部图像中提取脸部,以获取仅包含脸部的图像的最小可能尺寸,我想将这项任务应用到使用卷积神经网络进行训练的过程中。例如:
源代码:
import numpy as np
import cv2
face_cascade =cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
img=cv2.imread('swaa.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3,5,maxSize=(150,150))
for (x,y,w,h) in faces:
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
cv2.imwrite("imgg1.png",roi_color)
cv2.imwrite("imggg1.png",roi_gray)
cv2.imshow('img',img)
cv2.imwrite("img1.png",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
只需使用cv2.resize函数。 https://www.tutorialkart.com/opencv/python/opencv-python-resize-image/
import numpy as np
import cv2
H = 64
W = 64
face_cascade =cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
img=cv2.imread('swaa.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3,5,maxSize=(150,150))
for (x,y,w,h) in faces:
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = cv2.resize(gray[y:y+h, x:x+w], (H, W))
roi_color = cv2.resize(img[y:y+h, x:x+w], (H,W))
cv2.imwrite("imgg1.png",roi_color)
cv2.imwrite("imggg1.png",roi_gray)
cv2.imshow('img',img)
cv2.imwrite("img1.png",img)
cv2.waitKey(0)
cv2.destroyAllWindows()