File size: 8,722 Bytes
00f8748 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
# firstly import the necessary libraries :
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import zipfile
from os import listdir
from PIL import Image
from numpy import asarray,expand_dims
from matplotlib import pyplot
from keras.models import load_model
from keras_facenet import FaceNet
import pickle
from mtcnn import MTCNN
import math
# we are going to use harr cacade first
HaarCascade = cv2.CascadeClassifier(cv2.samples.findFile(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'))
# if harr cascade is unable to detect we will keep mtcnn for that case
# Initialize the MTCNN detector
mtcnn = MTCNN()
# we are going to use Facenet architecture for creating the embeddings from faces
model_face = FaceNet()
def process_image(image_path):
image = cv2.imread(image_path,cv2.IMREAD_UNCHANGED)
# for this example we are not resizing the image dimensions :
resized=image
image_rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
# we need to adjust the size of window in cv 2 to display the image
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray_image = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
cv2.namedWindow("output", cv2.WINDOW_NORMAL)
cv2.resizeWindow("output", resized.shape[0],resized.shape[1])
for (x, y, w, h) in faces:
cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow("output", image_rgb)
# cv2.waitKey(0)
cv2.destroyAllWindows()
# convert the image back to RGB format and adjust the brighness and contrast after processing
final = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2RGB)
final = cv2.convertScaleAbs(final, alpha=1, beta=0) # Adjust alpha and beta as needed
# save the image with bounding boxes as image_detected.jpg
cv2.imwrite('image_detected.jpg',final)
folder_name = 'attendance_folder'
if not os.path.exists(folder_name):
os.mkdir(folder_name)
# List all files in the folder
file_list = os.listdir(folder_name)
face_images = []
# Iterate through the files and remove them
for file in file_list:
file_path = os.path.join(folder_name, file)
if os.path.isfile(file_path):
os.remove(file_path)
# Save the cropped photos in the folder named attendance_class
for (x, y, w, h) in faces:
face_crop = resized[y:y+h, x:x+w]
face_images.append(face_crop)
face_filename = os.path.join(folder_name, f'face_{x}_{y}.jpg')
cv2.imwrite(face_filename, face_crop)
# we need to adjust the size of window in cv 2 to display the image
# folder_name = 'attendance_folder'
# if not os.path.exists(folder_name):
# os.mkdir(folder_name)
# List all files in the folder
# file_list = os.listdir(folder_name)
# face_images = []
# # Iterate through the files and remove them
# for file in file_list:
# file_path = os.path.join(folder_name, file)
# if os.path.isfile(file_path):
# os.remove(file_path)
# cv2.namedWindow("output", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("output", resized.shape[0],resized.shape[1])
# for face in faces:
# x, y, w, h = face['box']
# cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 255, 0), 2)
# cv2.imshow("output", image_rgb)
# # cv2.waitKey(0)
# face_crop = resized[y:y+h, x:x+w]
# face_images.append(face_crop)
# face_filename = os.path.join(folder_name, f'face_{x}_{y}.jpg')
# cv2.imwrite(face_filename, face_crop)
# cv2.destroyAllWindows()
# # convert the image back to RGB format and adjust the brighness and contrast after processing
# final = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2RGB)
# final = cv2.convertScaleAbs(final, alpha=1, beta=0) # Adjust alpha and beta as needed
# # save the image with bounding boxes as image_detected.jpg
# cv2.imwrite('image_detected.jpg',final)
def intermediate_process(gbr1):
# detect the face in the cropped photo :
harr = HaarCascade.detectMultiScale(gbr1,1.1,4)
# if the face is detected then get the width and height
if len(harr)>0:
x1, y1, width, height = harr[0]
# if harr cascade is unable to detect the face use mtcnn
else:
faces_mtcnn = mtcnn.detect_faces(gbr1)
if len(faces_mtcnn)>0:
x1, y1, width, height = faces_mtcnn[0]['box']
else :
# if no face is detected in the image just use the top left 10x10 pixels
x1, y1, width, height = 1, 1, 10, 10
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + width, y1 + height
#convert from bgr to rgb
gbr = cv2.cvtColor(gbr1, cv2.COLOR_BGR2RGB)
gbr = Image.fromarray(gbr) # Convert from OpenCV to PIL
# convert image as numpy array
gbr_array = asarray(gbr)
# crop the face , resize it and store in face
face = gbr_array[y1:y2, x1:x2]
face = Image.fromarray(face)
face = face.resize((160, 160))
face = asarray(face)
return gbr, face
def generate_embeddings(zip_path):
folder_name = os.path.splitext(zip_path)[0]
# Create the directory if it does not exist
if not os.path.exists(folder_name):
os.makedirs(folder_name)
# Unzip the file
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(folder_name)
folder=folder_name+'/'
# now generate the embeddings :
# intialize empty dictionary in which we will store the embeddings with name of the person
database = {}
# iterate through all the images in the training images folder
for filename in listdir(folder):
path = folder + filename
gbr1 = cv2.imread(folder + filename)
gbr, face = intermediate_process(gbr1)
# facenet takes as input 4 dimensional array so we expand dimension
face = expand_dims(face, axis=0)
signature = model_face.embeddings(face)
# store the array in the database
database[os.path.splitext(filename)[0]] = signature
cv2.destroyAllWindows()
# make a file named data_processed.pkl and store the database in it
myfile = open("embeddings.pkl", "wb")
pickle.dump(database, myfile)
myfile.close()
def recognize_faces(embeddigns_path,date):
myfile = open(embeddigns_path, "rb")
database = pickle.load(myfile)
myfile.close()
# same procedure as training
folder = 'attendance_folder/'
file_list = os.listdir(folder)
predicted=[]
# Set up the plot
num_images = len(file_list)
num_rows = math.ceil(num_images / 4) if math.ceil(num_images / 4)>0 else 1 # Ceiling division to calculate the number of rows
fig, axes = plt.subplots(num_rows, 4, figsize=(16, 4*num_rows))
if(num_rows==1):
axes=axes.reshape(1,4)
for i,filename in enumerate(file_list):
path = os.path.join(folder, filename)
gbr1 = cv2.imread(folder + filename)
gbr,face = intermediate_process(gbr1)
face = expand_dims(face, axis=0)
signature = model_face.embeddings(face)
min_dist=100
identity=' '
for key, value in database.items() :
dist = np.linalg.norm(value-signature)
if dist < min_dist:
min_dist = dist
identity = key
# Plot the image with the identity text
row = i // 4
col = i % 4
axes[row, col].imshow(gbr)
axes[row, col].set_title(f"Identity: {identity}", fontsize=25)
axes[row, col].axis('off')
# print(identity)
# cv2.namedWindow("output", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("output", gbr1.shape[0],gbr1.shape[1])
# cv2.putText(gbr1,identity, (100,100),cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2, cv2.LINE_AA)
# cv2.rectangle(gbr1,(x1,y1),(x2,y2), (0,255,0), 2)
# cv2.imshow("output",gbr1)
# cv2.waitKey(0)
predicted.append(identity)
# Hide any remaining empty subplots
for i in range(num_images, num_rows * 4):
row = i // 4
col = i % 4
axes[row, col].axis('off')
plt.tight_layout()
fig.savefig('image_grid.jpg')
cv2.destroyAllWindows()
# store the name of people present in a text file
attendance = [name for name in predicted if name != 'unknown']
file_name = f"{date}.txt"
with open(file_name, 'w') as file:
for item in attendance:
file.write(str(item) + '\n')
|