Face-Recognition-Using-CNN/data_creating.py at main · vrushankdhande/Face-Recognition-Using-CNN · GitHub

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# -*- coding: utf-8 -*-
"""data_creating.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1EfMZWOKBd-xZV_XtJ22sd9yGrMHGgMPd
"""

# Commented out IPython magic to ensure Python compatibility.
# only for video to photo converter
# 1 seconds = 1000 milliseconds.
import cv2
vidcap = cv2.VideoCapture('/content/gdrive/MyDrive/jyjyj/VID20210314210243.mp4')
def getFrame(sec):
    vidcap.set(cv2.CAP_PROP_POS_MSEC,sec*1000)               # just cue to 1 sec. position
    hasFrames,image = vidcap.read()
    if hasFrames:
        cv2.imwrite("/content/gdrive/MyDrive/jj/"+str(count)+".jpg", image)     # save frame as JPG file
    return hasFrames
sec = 0
frameRate = 0.02 #//it will capture image in each 0.5 second
count=1
success = getFrame(sec)
while success:
    count = count + 1
    sec = sec + frameRate
    sec = round(sec, 2)
    success = getFrame(sec)


# if the photoes are big then this will be used to work on one image and adjust proper for CNN
from matplotlib.pyplot import imshow
import numpy as np
from PIL import Image
# %matplotlib inline

path='/content/gdrive/MyDrive/jj/'                   #the path of images
path_resize='/content/gdrive/MyDrive/jyjyj/'             #path of resized images

testim = Image.open(path+"1.jpg", "r")
testim_rotate = testim.transpose(Image.ROTATE_90)
testim_resize = testim_rotate.resize((130, 175), Image.BOX)
imshow(np.asarray(testim_resize))
testim_resize.save(path_resize+"image_1.jpg")

#after getting proper image for CNN then it code to do for the rest of images
import os
import numpy as np
from PIL import Image

#read paths
source_path="/content/gdrive/MyDrive/jj/"         #the path of images
dest_path="/content/gdrive/MyDrive/kk/"       #path of resized images

# declare count
count=0
# read all images from source path
for count,image_file in enumerate(os.listdir(source_path)):
    image_path = os.path.join(source_path, image_file)
    # resizing of image
    testim = Image.open(image_path, "r")
    testim_rotate = testim.transpose(Image.ROTATE_90)
    testim_resize = testim_rotate.resize((130, 175), Image.BOX)
    testim_resize.save(dest_path+str(count)+".jpg")

# the program will extract the faces from the images /// for only one folder

import cv2
import sys
import os
import numpy as np
from PIL import Image as pil_image
from google.colab.patches import cv2_imshow

count=0

harcascadePath = '/content/gdrive/MyDrive/face_recognition/haarcascade_frontalface_default.xml'
detector = cv2.CascadeClassifier(harcascadePath)
th = '/content/gdrive/MyDrive/face_recognition/Faces/bros/Vrushank/'
imagePaths = [os.path.join(th,f) for f in os.listdir(th)]
new = '/content/gdrive/MyDrive/dd/'
face_arrr = []
label_arrr = []
try:
    for path in imagePaths:
        image = cv2.imread(path)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        faces = detector.detectMultiScale(gray, 1.3, 5)
        if faces is not ():
            for (x,y,w,h) in faces:
                #cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),1,1)
                face = image[y-50:y+h+50,x-50:x+w+50]
                count += 1
                # cv2.imwrite(new+str(count)+ 'face' + ".jpg",face)
                face_arrr.append(face)
                label_arrr.append(faces)


except:
    print("Oops!", sys.exc_info()[0], "occured.")
print("done")

# cv2_imshow(face)
# #  cv2.waitkey(0)
#  print(len(face))
#  print(face.shape)