Commit a99115ef authored by Mounica Vattumilli 's avatar Mounica Vattumilli
Browse files


parent d832bca2
...@@ -28,4 +28,156 @@ ...@@ -28,4 +28,156 @@
( (
( (
1. We need to import face recognition, computer vision (open cv) and numpy.
2. Then we need to install the images which need to be detected.
3. Load all the images by inserting the path of each file.
4. Now create array for the images and the respective names by using numpy files.
5. Encode all the images respectively for the known faces.
6. Initialization of some variables such as face location, face names, process all these in a frame.
7. For faster recognition resize the image to 1/4th size.
8. Convert the image from ‘bgr’ to ‘rgb’.
9. If the face detected is same as the face recognized then it should resize the image to its original dimensions.
10. Find all the faces in the camera and draw a rectangular box with the name should be displayed below the respective image.
import face_recognition
import cv2
import numpy as np
# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
# 2. Only detect faces in every other frame of video.
# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.
sandy_image = face_recognition.load_image_file("C:\\Users\\91964\\Pictures\\Camera Roll\\PIC.jpeg")
sandy_face_encoding = face_recognition.face_encodings(sandy_image)[0]
# Load a sample picture and learn how to recognize it.
image4_image = face_recognition.load_image_file("C:\\Users\\91964\\Pictures\\Camera Roll\\sir.jpg")
image4_face_encoding = face_recognition.face_encodings(image4_image)[0]
# Load a sample picture and learn how to recognize it.
image5_image = face_recognition.load_image_file("C:\\Users\\91964\\Pictures\\Camera Roll\\mouni.jpg")
image5_face_encoding = face_recognition.face_encodings(image5_image)[0]
# Load a second sample picture and learn how to recognize it.
image1_image = face_recognition.load_image_file("C:\\Users\\91964\\Pictures\\Camera Roll\\bharath.jpg")
image1_face_encoding = face_recognition.face_encodings(image1_image)[0]
# Load a second sample picture and learn how to recognize it.
image2_image = face_recognition.load_image_file("C:\\Users\\91964\\Pictures\\Camera Roll\\harsha.jpg")
image2_face_encoding = face_recognition.face_encodings(image2_image)[0]
# Load a second sample picture and learn how to recognize it.
image3_image = face_recognition.load_image_file("C:\\Users\\91964\\Pictures\\Camera Roll\\maanya.jpg")
image3_face_encoding = face_recognition.face_encodings(image3_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
known_face_names = [
"Sripath roy sir",
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
# Grab a single frame of video
ret, frame =
# Resize frame of video to 1/4 size for faster face recognition processing MJ87Y
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 = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# 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)
face_names = []
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)
if matches[best_match_index]:
name = known_face_names[best_match_index]
process_this_frame = not process_this_frame
# Display the results
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), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
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'):
# Release handle to the webcam
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