Added a DLib script and references to Face Recognition

Resources/dlib_face_reg.py

@@ -0,0 +1,89 @@
+# import the necessary packages
+from imutils.video import VideoStream
+import face_recognition
+import argparse
+import imutils
+import pickle
+import time
+import cv2
+# construct the argument parser and parse the arguments
+ap = argparse.ArgumentParser()
+
+ap.add_argument("-o", "--output", type=str,
+	help="path to output video")
+ap.add_argument("-y", "--display", type=int, default=1,
+	help="whether or not to display output frame to screen")
+ap.add_argument("-d", "--detection-method", type=str, default="hog",
+	help="face detection model to use: either `hog` or `cnn`")
+args = vars(ap.parse_args())
+
+# load the known faces and embeddings
+# initialize the video stream and pointer to output video file, then
+# allow the camera sensor to warm up
+print("[INFO] starting video stream...")
+vs = VideoStream(src=0).start()
+writer = None
+time.sleep(2.0)
+
+# loop over frames from the video file stream
+while True:
+	# grab the frame from the threaded video stream
+	frame = vs.read()
+
+	# convert the input frame from BGR to RGB then resize it to have
+	# a width of 750px (to speedup processing)
+	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+	rgb = imutils.resize(frame, width=750)
+	r = frame.shape[1] / float(rgb.shape[1])
+	# detect the (x, y)-coordinates of the bounding boxes
+	# corresponding to each face in the input frame, then compute
+	# the facial embeddings for each face
+	boxes = face_recognition.face_locations(rgb,
+		model=args["detection_method"])
+	encodings = face_recognition.face_encodings(rgb, boxes)
+
+
+
+	# loop over the recognized faces
+	for (top, right, bottom, left) in boxes:
+		# rescale the face coordinates
+		top = int(top * r)
+		right = int(right * r)
+		bottom = int(bottom * r)
+		left = int(left * r)
+		# draw the predicted face name on the image
+		cv2.rectangle(frame, (left, top), (right, bottom),
+			(0, 255, 0), 2)
+		y = top - 15 if top - 15 > 15 else top + 15
+
+
+	# if the video writer is None *AND* we are supposed to write
+	# the output video to disk initialize the writer
+	if writer is None and args["output"] is not None:
+		fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+		writer = cv2.VideoWriter(args["output"], fourcc, 20,
+			(frame.shape[1], frame.shape[0]), True)
+	# if the writer is not None, write the frame with recognized
+	# faces to disk
+	if writer is not None:
+		writer.write(frame)
+
+	# check to see if we are supposed to display the output frame to
+	# the screen
+	if args["display"] > 0:
+		cv2.imshow("Frame", frame)
+		key = cv2.waitKey(1) & 0xFF
+		# if the `q` key was pressed, break from the loop
+		if key == ord("q"):
+			break
+
+# do a bit of cleanup
+cv2.destroyAllWindows()
+vs.stop()
+# check to see if the video writer point needs to be released
+if writer is not None:
+	writer.release()
+
+
+# python face_reg.py --output out/webcam_face_recognition_output.avi --display 1
+

Resources/readme.md

@@ -0,0 +1,11 @@
+This text file has all the resources that have been refered to while making of this project.
+
+## Face Identification
+- https://towardsdatascience.com/face-detection-models-which-to-use-and-why-d263e82c302c
+- https://towardsdatascience.com/face-recognition-on-330-million-images-at-400-images-per-second-b85e594eab66
+- https://pyimagesearch.com/2021/04/19/face-detection-with-dlib-hog-and-cnn/
+- Some Face Recognition Model:
+  1. [FaceNet]([url](https://arxiv.org/abs/1503.03832))
+  2. [DeepFace]([url](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf))
+
+  As of now we are working with DLib. A script for the same can be found in the Resources section.