Crops a folder of headshots to the face that it finds in the image.

crop-to-face.py

import os
import cv2
import matplotlib.pyplot as plt
from PIL import Image

# adjust these variables as necessary
dirname = "pix-for-crop"
put_dirname = "cropped"
simple_crop = False

# Check if the output directory exists, create it if it doesn't
if not os.path.exists(put_dirname):
    os.mkdir(put_dirname)

# Load the cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

file_types = ('.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG')

files = [file_i
         for file_i in os.listdir(dirname)
         if file_i.endswith(file_types)]

filenames = [os.path.join(dirname, fname)
             for fname in files]

print('found %d files' % len(filenames))

filename_inc = 100

filecount = 1

for file in filenames:
    img = cv2.imread(file)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    detected_faces = face_cascade.detectMultiScale(gray, 1.1, 4)
    print("[%d of %d] %d detected faces in %s" % (filecount, len(filenames), len(detected_faces), file))
    for (x, y, w, h) in detected_faces:
        crop_width = min(w, h)

        if w >= crop_width and h >= crop_width:
            image_to_crop = Image.open(file)

            if simple_crop:
                crop_area = (x, y, x+w, y+h)
            else:
                size_array = []
                size_array.append(y)
                size_array.append(image_to_crop.height - (y+h))
                size_array.append(x)
                size_array.append(image_to_crop.width - (x+w))
                size_array.sort()
                short_side = size_array[0]
                crop_area = (x - size_array[0] , y - size_array[0], x+w + size_array[0], y+h + size_array[0])

            cropped_image = image_to_crop.crop(crop_area)

            filename = os.path.basename(file)
            cropped_image.save(put_dirname + "/" + filename, "JPEG")
            filename_inc += 1
    filecount += 1