I got the following python code that uses OpenCV to take a given picture and then look for a face and crop that face and save it with the word “Cropped” appended to the file name. Although it’s working great, there’s two things I cannot seem to figure out how to do.
- It seems to be cropping very close to the face edges, I’d like to set an offset to convert it so that it fetches a passport like photo from the crop
- It seems to also add a red border.
Can anyone kindly advise how I can possibly solve these two issues?
Thanks
import cv2
img = cv2.imread('testImage.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h),
(0, 0, 255), 2)
faces = img[y:y + h, x:x + w]
cv2.imwrite('testImage-Cropped.jpg', faces)
cv2.waitKey()
2
- You need to remove the cv2.rectangle line, this is what adds the red rectangle around the face
- Add some margin when cropping the faces, limit to valid indices, play around with the value of “margin” to find the one you like
- cv2.waitKey() just hangs the execution, not sure why you need it
- Note that you’re overwriting the same file each time, i’ve added a counter so each face is saved in a different image file
The full example:
import cv2
img = cv2.imread('testImage.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
margin = 20
for i, (x, y, w, h) in enumerate(faces):
a = max(y - margin//2, 0)
b = min(y + h + margin//2, img.shape[0])
c = max(x - margin//2, 0)
d = min(x + w + margin//2, img.shape[1])
faces = img[a:b, c:d]
cv2.imwrite(f'testImage-Cropped_{i}.jpg', faces)
Edit: Per request I’ve added an example
original image:
source
collage of detected faces:
full code:
import cv2
import numpy as np
img = cv2.imread('solvay-bw.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade =
cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
margin = 20
imface = []
w_max, h_max = 0, 0
for i, (x, y, w, h) in enumerate(faces):
a = max(y - margin//2, 0)
b = min(y + h + margin//2, img.shape[0])
c = max(x - margin//2, 0)
d = min(x + w + margin//2, img.shape[1])
face = img[a:b, c:d]
imface.append(face)
w_max = max(b-a, w_max)
h_max = max(d-c, h_max)
cv2.imwrite(f'testImage-Cropped_{i}.jpg', face)
imdraw = np.zeros((h_max*4, int(w_max*np.ceil(len(faces)/4)), 3), dtype=np.uint8)
imdraw[..., :] = (242, 248, 248)
for i, face in enumerate(imface):
x = w_max*(i//4)
y = h_max*(i%4)
imdraw[y:y+face.shape[1], x:x+face.shape[0]] = face
cv2.imwrite(f'testImage-AllFaces.jpg', imdraw)
6