Develop Reference

Python CV2 video writer doesn't save video

I have a simple python code using OpenCV and Keras that performs some detections on frames (follow-up from my previous question here). But when I want to record and save the frames as a video using video_writer, the generated video is empty.
What is wrong in the video_writer?
#........some code
# start the webcam feed
cap = cv2.VideoCapture(1)
canvasImageOriginal = cv2.imread("fg2.png")
canvasImage = cv2.imread("fg2.png")
canvasHappy = cv2.imread("fg2happy.png")
canvasSad = cv2.imread("fg2sad.png")
x0, x1 = 330, 1290
y0, y1 = 155, 700
#=========
w=960#int(cap.get(cv2.CV_CAP_PROP_FRAME_WIDTH ))
h=540#int(cap.get(cv2.CV_CAP_PROP_FRAME_HEIGHT ))
# video recorder
fourcc = cv2.VideoWriter_fourcc(*'XVID')
video_writer = cv2.VideoWriter('output.avi', fourcc, 25.0, (w, h))
#=========
prediction_history = []
LOOKBACK = 5 # how far you want to look back
counter = 0
while True:
# Find haar cascade to draw bounding box around face
ret, frame = cap.read()
frame=cv2.flip(frame,3)
if not ret:
break
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
prediction = model.predict(cropped_img)
maxindex = int(np.argmax(prediction))
text = emotion_dict[maxindex]
print(prediction[0][3])
prediction_history.append(maxindex)
most_common_index = max(set(prediction_history[-LOOKBACK:][::-1]), key = prediction_history.count)
text = emotion_dict[most_common_index]
#if ("Sad" in text) or ("Angry" in text) or ("Disgusted" in text):
# text = "Sad"
if ("Happy" in text) or ("Sad" in text) :
cv2.putText(frame, text, (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
if ("Happy" in text):
counter= counter+1
if counter == 10:
#print("Happy!")
canvasImage = canvasHappy
else:
counter = 0
canvasImage = canvasImageOriginal
dim = (800,480)
frame_shrunk = cv2.resize(frame, (x1 - x0, y1 - y0))
canvasImage[y0:y1, x0:x1] = frame_shrunk
#cv2.imshow('Video', cv2.resize(frame,dim,interpolation = cv2.INTER_CUBIC))
cv2.imshow('Demo', canvasImage)
video_writer.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
video_writer.release()
cv2.destroyAllWindows()

As it is mentioned above, please check print(frame.shape).
When I did it, I saw (300,450,3), and I changed the resolution of videowriter as (450,300) and it worked for me. As a result, I can say that frame.shape=(y, x, maybe color) but the resolution of videowriter=(x,y).

Enrollment of new faces into face recognition dataset (opencv, face_recognition)

I have a face recognition code(Entire code given at the end) that works perfectly fine with the existing dataset.
But I wanted it to also add new faces into the dataset(Enrollments), after asking for a user input for the name of the new person in the frame [like this: new_name = print(Who is this?)]. So then I could create a new folder by the entered name, and store the face inside the frame. This is what I did:
new_name = input("Who is this?")
path_2 = os.path.join('Images',new_name)
os.mkdir(path_2)
print("Directory '% s' created" % new_name)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0,0,255),
thickness = 2)
cv2.putText(frame, new_name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
sub_face = frame[y:y+h, x:x+w]
FaceFileName = new_name + str(y+x) + ".jpg"
cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
cv2.imshow("Frame",frame)
#if cv2.waitKey(1) & 0xFF == ord('q'):
# break
This worked fine with new people. But now I had to do something for unrecognized faces of known people.
In this case, we would already have a folder by the entered name. We must append the image into the existing folder by the entered name.
So for this I tried the below code: (Did not work)
else: # To store the unknown new face with name
new_name = input("Who is this?")
# If the new_name entered already exists as a folder
if os.path.isfile(new_name):
print(new_name,"folder already exists")
frame = imutils.resize(frame, width = 400)
rects = detector.detectMultiScale(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
FaceFileName = new_name + str(y+x) + ".jpg"
for (x, y, w, h) in rects:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Frame",frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("k"):
p = os.path.join([new_name,FaceFileName.format(str(total).zfill(5))])
# cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
cv2.imwrite(p, orig)
total += 1
print("Image saved")
elif key == ord("q"):
break
# If the new_name does not exist as a folder new folder has to be created
else:
path_2 = os.path.join('Images',new_name)
os.mkdir(path_2)
print("Directory '% s' created" % new_name)
frame = imutils.resize(frame, width = 400)
rects = detector.detectMultiScale(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
FaceFileName = new_name + str(y+x) + ".jpg"
for (x, y, w, h) in rects:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Frame",frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("k"):
p = os.path.join([path_2,FaceFileName.format(str(total).zfill(5))])
# cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
cv2.imwrite(p, orig)
total += 1
print("Image saved")
I am getting the error:
Who is this?Vishwesh
Traceback (most recent call last):
File "C:\Users\Vishw\databs.py", line 117, in <module>
os.mkdir(path_2)
FileExistsError: [WinError 183] Cannot create a file when that file already exists: 'Images\\Vishwesh'
[ WARN:0] global C:\Users\runneradmin\AppData\Local\Temp\pip-req-build-sgoydvi3\opencv\modules\videoio\src\cap_msmf.cpp (438) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB terminating async callback
Complete code is given below. Let me know what is wrong with the code. Kindly help!
Note: I included the above codes under the # Face recognition on LIVE WEBCAM FEED section and as else statement for the "if True in matches:"
Here is my entire code:
# Extracting features from face
from imutils import paths
import face_recognition
import pickle
import cv2
import os
#Get paths of each file in folder named Images
#Images here contains my data(folders of various persons)
imagePaths = list(paths.list_images('Images'))
knownEncodings = []
knownNames = []
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
name = imagePath.split(os.path.sep)[-2]
# load the input image and convert it from BGR (OpenCV ordering)
# to dlib ordering (RGB)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#Use Face_recognition to locate faces
boxes = face_recognition.face_locations(rgb,model='hog')
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# loop over the encodings
for encoding in encodings:
knownEncodings.append(encoding)
knownNames.append(name)
#save emcodings along with their names in dictionary data
data = {"encodings": knownEncodings, "names": knownNames}
#use pickle to save data into a file for later use
f = open("face_enc", "wb")
f.write(pickle.dumps(data))
f.close()
# Face recognition on LIVE WEBCAM FEED
import face_recognition
import pickle
import cv2
import os
#find path of xml file containing haarcascade file
cascPathface = os.path.dirname(
cv2.__file__) + "/data/haarcascade_frontalface_default.xml"
# load the harcaascade in the cascade classifier
faceCascade = cv2.CascadeClassifier(cascPathface)
# load the known faces and embeddings saved in last file
data = pickle.loads(open('face_enc', "rb").read())
print("Streaming started")
video_capture = cv2.VideoCapture(0)
# loop over frames from the video file stream
while True:
# grab the frame from the threaded video stream
ret, frame = video_capture.read()
orig = frame.copy()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.05,
minNeighbors=3,
minSize=(60, 60),
flags=cv2.CASCADE_SCALE_IMAGE)
# convert the input frame from BGR to RGB
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# the facial embeddings for face in input
encodings = face_recognition.face_encodings(rgb)
names = []
# loop over the facial embeddings incase
# we have multiple embeddings for multiple fcaes
for encoding in encodings:
#Compare encodings with encodings in data["encodings"]
#Matches contain array with boolean values and True for the embeddings it matches closely
#and False for rest
matches = face_recognition.compare_faces(data["encodings"],encoding)
#set name =unknown if no encoding matches
name = "Unknown"
# check to see if we have found a match
if True in matches:
# Find positions at which we get True and store them
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
#Check the names at respective indexes we stored in matchedIdxs
name = data["names"][i]
#increase count for the name we got
counts[name] = counts.get(name, 0) + 1
#set name which has highest count
name = max(counts, key=counts.get)
else: # To store the unknown new face with name
new_name = input("Who is this?")
# If the new_name entered already exists as a folder
if os.path.isfile(new_name):
print(new_name,"folder already exists")
frame = imutils.resize(frame, width = 400)
rects = detector.detectMultiScale(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
FaceFileName = new_name + str(y+x) + ".jpg"
for (x, y, w, h) in rects:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Frame",frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("k"):
p = os.path.join([new_name,FaceFileName.format(str(total).zfill(5))])
# cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
cv2.imwrite(p, orig)
total += 1
print("Image saved")
elif key == ord("q"):
break
# If the new_name does not exist as a folder new folder has to be created
else:
path_2 = os.path.join('Images',new_name)
os.mkdir(path_2)
print("Directory '% s' created" % new_name)
frame = imutils.resize(frame, width = 400)
rects = detector.detectMultiScale(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
FaceFileName = new_name + str(y+x) + ".jpg"
for (x, y, w, h) in rects:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Frame",frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("k"):
p = os.path.join([path_2,FaceFileName.format(str(total).zfill(5))])
# cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
cv2.imwrite(p, orig)
total += 1
print("Image saved")
# update the list of names
names.append(name)
# loop over the recognized faces
for ((x, y, w, h), name) in zip(faces, names):
# rescale the face coordinates
# draw the predicted face name on the image
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
cv2.imshow("Frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()

So, your issue is rather related to file operations then to face recognition...
Try to check if folder alreday exists before trying to create it:
path_2 = os.path.join('Images',new_name)
if not os.path.exists(path_2):
os.mkdir(path_2)

Using the idea given by #Bohdan:
else: # To store the unknown new face with name
new_name = input("Who is this?")
path_2 = os.path.join('Images',new_name)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0,0,255),
thickness = 2)
cv2.putText(frame, new_name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
sub_face = frame[y:y+h, x:x+w]
FaceFileName = new_name + str(y+x) + ".jpg"
if not os.path.exists(path_2):
os.mkdir(path_2)
print("Directory '% s' created" % new_name)
cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
# To store unrecognised faces of known people
else:
cv2.imwrite(os.path.join(path_2,FaceFileName),sub_face)
Code works perfectly fine!

Opencv 4.0+ Displaying images as video stream crashes the system (How to add FPS while displaying images as Video stream)

I am trying KCF with Opencv 4.0 and using it on images. I have a directory where I have saved all the images of a videos. One idea is to first convert all the images in the directory with a desired FPS using: cv2.VideoWriter()
But I don't see a sense in that when I have already opened the images. My system crashes every time. I think because of the continuous images moving so rapidly. I need to add a FPS to the stream. Below is the code for the project. Please help.
files = sorted(glob.glob("data/*.jpg"))
tracker = cv2.TrackerKCF_create()
initBB = None
fps = None
for i in files:
frame = cv2.imread(i)
frame = imutils.resize(frame, width=500)
(H, W) = frame.shape[:2]
if initBB is not None:
(success, box) = tracker.update(frame)
if success:
(x, y, w, h) = [int(v) for v in box]
cv2.rectangle(frame, (x, y), (x + w, y + h),(0, 255, 0), 2)
info = [
("Tracker", 'kcf'),
("Success", "Yes" if success else "No"),
]
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("s"):
initBB = cv2.selectROI("Frame", frame, fromCenter=False,showCrosshair=True)
tracker.init(frame, initBB)
elif key == ord("q"):
break
cv2.destroyAllWindows()

Face Recoginition Python open cv

Is there any way i can make my own train set for face recognition in python ? To be more specific i want to make a train set like an AT&T Face database. I want my camera to take 20 images of each person(30 max) and store it in the separate folders by the name of each person.
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
fn_name = sys.argv[1]
path = os.path.join(fn_dir, fn_name)
if not os.path.isdir(path):
os.mkdir(path)
(im_width, im_height) = (112, 92)
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
# The program loops until it has 20 images of the face.
count = 0
while count < 20:
(rval, im) = webcam.read()
im = cv2.flip(im, 1, 0)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
faces = haar_cascade.detectMultiScale(mini)
faces = sorted(faces, key=lambda x: x[3])
if faces:
face_i = faces[0]
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
pin=sorted([int(n[:n.find('.')]) for n in os.listdir(path)
if n[0]!='.' ]+[0])[-1] + 1
cv2.imwrite('%s/%s.png' % (path, pin), face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.putText(im, fn_name, (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN,
1,(0, 255, 0))
count += 1
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break

For this, you just need to provide a particular path to save all the image with (.png) or (.bmp) or (.jpg) extension in a sorted manner.
# train.py
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'face_data'
fn_name = sys.argv[0]
path = os.path.join(fn_dir, fn_name)
(im_width, im_height) = (112, 92)
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
# Generate name for image file
pin=sorted([int(n[:n.find('.')]) for n in os.listdir(path)
if n[0]!='.' ]+[0])[-1] + 1
# Beginning message
print("\n\033[94mThe program will save 20 samples. \
Move your head around to increase while it runs.\033[0m\n")
# The program loops until it has 20 images of the face.
count = 0
pause = 0
count_max = 20
while count < count_max:
# Loop until the camera is working
rval = False
while(not rval):
# Put the image from the webcam into 'frame'
(rval, frame) = webcam.read()
if(not rval):
print("Failed to open webcam. Trying again...")
# Get image size
height, width, channels = frame.shape
# Flip frame
frame = cv2.flip(frame, 1, 0)
# Convert to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Scale down for speed
mini = cv2.resize(gray, (int(gray.shape[1] / size), int(gray.shape[0] / size)))
# Detect faces
faces = haar_cascade.detectMultiScale(mini)
# We only consider largest face
faces = sorted(faces, key=lambda x: x[3])
if faces:
face_i = faces[0]
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
# Draw rectangle and write name
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.putText(frame, fn_name, (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN,
1,(0, 255, 0))
# Remove false positives
if(w * 6 < width or h * 6 < height):
print("Face too small")
else:
# To create diversity, only save every fith detected image
if(pause == 0):
print("Saving training sample "+str(count+1)+"/"+str(count_max))
# Save image file
cv2.imwrite('%s/%s.png' % (path, pin), face_resize)
pin += 1
count += 1
pause = 1
if(pause > 0):
pause = (pause + 1) % 5
cv2.imshow('OpenCV', frame)
key = cv2.waitKey(10)
if key == 27:
break
This code will help you to get the cropped images from the webcam and store them in a directory name as face_data for training purpose.
In case, you don't want to train your dataset from webcam, you can simply do one thing:
that just create a directory and create 5-6 sub-directory in it as in Happy, Sad, Angry, Neutral, Calm, etc.
Download the images and put them in corresponding folders for training purpose, now follow this code.
## This program first ensures if the face of a person exists in the given image or
not then if it exists, it crops
## the image of the face and saves to the given directory.
## Importing Modules
import cv2
import os
directory = "C:\\Users\\hp"
## directory where the images to be saved:
f_directory = "C:\\Users\\hp\\face_data/"
def facecrop(image):
## Crops the face of a person from an image!
## OpenCV XML FILE for Frontal Facial Detection using HAAR CASCADES.
facedata=
"C:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_default.xml"
cascade = cv2.CascadeClassifier(facedata)
## Reading the given Image with OpenCV
img = cv2.imread(image)
try:
minisize = (img.shape[1],img.shape[0])
miniframe = cv2.resize(img, minisize)
faces = cascade.detectMultiScale(miniframe)
for f in faces:
x, y, w, h = [ v for v in f ]
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
sub_face = img[y:y+h, x:x+w]
f_name = image.split('/')
f_name = f_name[-1]
## Change here the Desired directory.
cv2.imwrite(f_directory + f_name, sub_face)
print ("Writing: " + image)
except:
pass
if __name__ == '__main__':
images = os.listdir(directory)
i = 0
for img in images:
file = directory + img
print (i)
facecrop(file)
i += 1

Python+OpenCV: cv2.imwrite

I'm trying to detect a face and write down area with the face in a separate file.
How can I do it? I think that i must use "faces" (you can see this var in code). But how?
from ffnet import mlgraph, ffnet, tmlgraph, imlgraph
import pylab
import sys
import cv,cv2
import numpy
cascade = cv.Load('C:\opencv\data\haarcascades\haarcascade_frontalface_alt.xml')
def detect(image):
bitmap = cv.fromarray(image)
faces = cv.HaarDetectObjects(bitmap, cascade, cv.CreateMemStorage(0))
if faces:
for (x,y,w,h),n in faces:
cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,255),3)
return image
if __name__ == "__main__":
cam = cv2.VideoCapture(0)
while 1:
_,frame =cam.read()
frame = numpy.asarray(detect(frame))
cv2.imshow("features", frame)
if cv2.waitKey(1) == 0x1b: # ESC
print 'ESC pressed. Exiting ...'
break

This following code should extract face in images and save faces on disk
def detect(image):
image_faces = []
bitmap = cv.fromarray(image)
faces = cv.HaarDetectObjects(bitmap, cascade, cv.CreateMemStorage(0))
if faces:
for (x,y,w,h),n in faces:
image_faces.append(image[y:(y+h), x:(x+w)])
#cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,255),3)
return image_faces
if __name__ == "__main__":
cam = cv2.VideoCapture(0)
while 1:
_,frame =cam.read()
image_faces = []
image_faces = detect(frame)
for i, face in enumerate(image_faces):
cv2.imwrite("face-" + str(i) + ".jpg", face)
#cv2.imshow("features", frame)
if cv2.waitKey(1) == 0x1b: # ESC
print 'ESC pressed. Exiting ...'
break

Alternatively, with MTCNN and OpenCV(other dependencies including TensorFlow also required), you can:
1 Perform face detection(Input an image, output all boxes of detected faces):
from mtcnn.mtcnn import MTCNN
import cv2
face_detector = MTCNN()
img = cv2.imread("Anthony_Hopkins_0001.jpg")
detect_boxes = face_detector.detect_faces(img)
print(detect_boxes)
[{'box': [73, 69, 98, 123], 'confidence': 0.9996458292007446, 'keypoints': {'left_eye': (102, 116), 'right_eye': (150, 114), 'nose': (129, 142), 'mouth_left': (112, 168), 'mouth_right': (146, 167)}}]
2 save all detected faces to separate files:
for i in range(len(detect_boxes)):
box = detect_boxes[i]["box"]
face_img = img[box[1]:(box[1] + box[3]), box[0]:(box[0] + box[2])]
cv2.imwrite("face-{:03d}.jpg".format(i+1), face_img)
3 or Draw rectangles of all detected faces:
for box in detect_boxes:
box = box["box"]
pt1 = (box[0], box[1]) # top left
pt2 = (box[0] + box[2], box[1] + box[3]) # bottom right
cv2.rectangle(img, pt1, pt2, (0,255,0), 2)
cv2.imwrite("detected-boxes.jpg", img)

wtluo, great !
May I propose a slight modification of your code 2. ? Here it is:
for i, detected_box in enumerate(detect_boxes):
box = detected_box["box"]
face_img = img[ box[1]:box[1] + box[3], box[0]:box[0] + box[2] ]
cv2.imwrite("face-{:03d}.jpg".format(i+1), face_img)