i want to save the detected face in dlib by cropping the rectangle do anyone have any idea how can i crop it. i am using dlib first time and having so many problems. i also want to run the fisherface algorithm on the detected faces but it is giving me type error when i pass the detected rectangle to pridictor. i seriously need help in this issue.
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]))
Should be like this:
crop_img = img_full[d.top():d.bottom(),d.left():d.right()]