How to save / crop detected faces in python dlib

Question

I want to save the detected face in dlib by trimming the rectangle do anyone know how I can crop it. I am using dlib for the first time and having so many problems. I also want to run the detected faces algorithm, but it gives me a type error when I pass the detected rectangle to the pridictor. I really need help in this matter.

import cv2, sys, numpy, os import dlib from skimage import io import json import uuid import random from datetime import datetime from random import randint #predictor_path = sys.argv[1] fn_haar = 'haarcascade_frontalface_default.xml' fn_dir = 'att_faces' size = 4 detector = dlib.get_frontal_face_detector() #predictor = dlib.shape_predictor(predictor_path) options=dlib.get_frontal_face_detector() options.num_threads = 4 options.be_verbose = True win = dlib.image_window() # Part 1: Create fisherRecognizer print('Training...') # Create a list of images and a list of corresponding names (images, lables, names, id) = ([], [], {}, 0) for (subdirs, dirs, files) in os.walk(fn_dir): for subdir in dirs: names[id] = subdir subjectpath = os.path.join(fn_dir, subdir) for filename in os.listdir(subjectpath): path = subjectpath + '/' + filename lable = id images.append(cv2.imread(path, 0)) lables.append(int(lable)) id += 1 (im_width, im_height) = (112, 92) # Create a Numpy array from the two lists above (images, lables) = [numpy.array(lis) for lis in [images, lables]] # OpenCV trains a model from the images model = cv2.createFisherFaceRecognizer(0,500) model.train(images, lables) haar_cascade = cv2.CascadeClassifier(fn_haar) webcam = cv2.VideoCapture(0) webcam.set(5,30) while True: (rval, frame) = webcam.read() frame=cv2.flip(frame,1,0) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size)) dets = detector(gray, 1) print "length", len(dets) print("Number of faces detected: {}".format(len(dets))) for i, d in enumerate(dets): print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format( i, d.left(), d.top(), d.right(), d.bottom())) cv2.rectangle(gray, (d.left(), d.top()), (d.right(), d.bottom()), (0, 255, 0), 3) ''' #Try to recognize the face prediction = model.predict(dets) print "Recognition Prediction" ,prediction''' win.clear_overlay() win.set_image(gray) win.add_overlay(dets) if (len(sys.argv[1:]) > 0): img = io.imread(sys.argv[1]) dets, scores, idx = detector.run(img, 1, -1) for i, d in enumerate(dets): print("Detection {}, score: {}, face_type:{}".format( d, scores[i], idx[i])) 

Answer 1

It should be like this:

 crop_img = img_full[d.top():d.bottom(),d.left():d.right()] 

Answer 2

Please use the minimum working code for quick answers.

After you find the face, you have a rectangle. This way you can crop the image and save using opencv functions :

  img = cv2.imread("test.jpg") dets = detector.run(img, 1) for i, d in enumerate(dets): print("Detection {}, score: {}, face_type:{}".format( d, scores[i], idx[i])) crop = img[d.top():d.bottom(), d.left():d.right()] cv2.imwrite("cropped.jpg", crop) 

Answer 3

Andrey's answer was good, but it skips extreme cases where the original rectangle is partially outside the image window. (Yes, what happens with dlib.)

 crop_img = img_full[max(0, d.top()): min(d.bottom(), image_height), max(0, d.left()): min(d.right(), image_width)]