Mitul Mohammad Abdullah Al Mukit
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import face_recognition
import cv2
import numpy as np
import imageSegmentation
from mediapipe.tasks.python import vision
import Visualization_utilities as vis
# Get a reference to webcam #0 (the default one)
# video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.
def get_face_encoding(path):
print(f'path: {path}')
print('hello')
HKID_cropped = imageSegmentation.auto_cropping(path)
cv2.imwrite('saved/HKID.jpg', HKID_cropped)
HKID_image = face_recognition.load_image_file("saved/HKID.jpg")
HKID_face_encoding = face_recognition.face_encodings(HKID_image)[0]
return HKID_face_encoding
# HKID_image = face_recognition.load_image_file("saved/HKID.jpg")
# HKID_face_encoding = face_recognition.face_encodings(HKID_image)[0]
# Create arrays of known face encodings and their names
# known_face_encodings = [
# HKID_face_encoding
# ]
# known_face_names = [
# "Marco"
# ]
# Initialize some variables
# face_locations = []
# face_encodings = []
# face_names = []
# process_this_frame = True
# score = []
# faces = 0 # number of faces
# while True:
# # Grab a single frame of video
# ret, frame = video_capture.read()
# # # Draw a label with a name below the face
# # cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
# # font = cv2.FONT_HERSHEY_DUPLEX
# # cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# # Display the resulting image
# cv2.imshow('Video', frame)
# # Hit 'q' on the keyboard to quit!
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
def process_frame(frame, process_this_frame, face_locations, faces, face_names, score):
hkid_face_encoding = get_face_encoding("image")
print(f'encoding: {hkid_face_encoding}')
known_face_encodings = [
hkid_face_encoding
]
known_face_names = [
"recognized"
]
# Only process every other frame of video to save time
if process_this_frame:
face_names = []
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
faces = len(face_encodings) # number of faces
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
print(face_distances)
if matches[best_match_index] and face_distances[best_match_index] < 0.45:
score.append(face_distances[best_match_index])
name = known_face_names[best_match_index]
else:
score = []
face_names.append(name)
# if len(score) > 20:
# avg_score = sum(score) / len(score)
# Display the results
if faces > 1 :
# Define the text and font properties
text = "More than 1 person detected!"
font = cv2.FONT_HERSHEY_DUPLEX
font_scale = 1
font_thickness = 2
# Calculate the text size
window_height = frame.shape[0]
window_width = frame.shape[1]
text_size, _ = cv2.getTextSize(text, font, font_scale, font_thickness)
# Calculate the text position
text_x = int((window_width - text_size[0]) / 2)
text_y = window_height - int(text_size[1] / 2)
cv2.putText(frame, text, (text_x, text_y), font, font_scale, (255, 255, 255), font_thickness, cv2.LINE_AA)
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (65, 181, 41), 4)
# Define the name box properties
name_box_color = (44, 254, 0)
name_box_alpha = 0.7
name_box_thickness = -1
# Define the text properties
font = cv2.FONT_HERSHEY_TRIPLEX
font_scale = 1
font_thickness = 2
text_color = (255, 255, 255)
# Calculate the text size
text_width, text_height = cv2.getTextSize(name, font, font_scale, font_thickness)[0]
# Draw the name box
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
name_box_color, name_box_thickness)
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
name_box_color, cv2.FILLED)
# Draw the name text
cv2.putText(frame, name, (left + 70, bottom - 6), font, font_scale, text_color, font_thickness)
process_this_frame = process_this_frame
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
return frame, process_this_frame, face_locations, faces, face_names, score
def convert_distance_to_percentage(distance, threshold):
if distance < threshold:
score = 80
score += distance / 0.45 * 20
else:
score = (1 - distance) * 100
return score
# percent = convert_distance_to_percentage(avg_score, 0.45)
# print(f'avg_score = {percent:.2f}% : Approved!')
# # Release handle to the webcam
# video_capture.release()
# cv2.destroyAllWindows()