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examples/face-detection/embeddings.py

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+import sys
+
+#sys.path.append('/home/shivam/Downloads/lib/python3.6/site-packages')
+sys.path.append('/home/shivam/Downloads/face-recognition/lib/python3.6/site-packages')
+import cv2 
+import face_recognition
+import pickle
+name=input("enter name")
+ref_id=input("enter id")
+
+try:
+	f=open("ref_name.pkl","rb")
+
+	ref_dictt=pickle.load(f)
+	f.close()
+except:
+	ref_dictt={}
+ref_dictt[ref_id]=name
+
+
+f=open("ref_name.pkl","wb")
+pickle.dump(ref_dictt,f)
+f.close()
+
+try:
+	f=open("ref_embed.pkl","rb")
+
+	embed_dictt=pickle.load(f)
+	f.close()
+except:
+	embed_dictt={}
+
+
+
+
+
+for i in range(5):
+	key = cv2. waitKey(1)
+	webcam = cv2.VideoCapture(0)
+	while True:
+
+		check, frame = webcam.read()
+		# print(check) #prints true as long as the webcam is running
+		# print(frame) #prints matrix values of each framecd 
+		cv2.imshow("Capturing", frame)
+		small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+		rgb_small_frame = small_frame[:, :, ::-1]
+
+		key = cv2.waitKey(1)
+
+		if key == ord('s') : 
+			face_locations = face_recognition.face_locations(rgb_small_frame)
+			if face_locations != []:
+
+				# filename="photo.jpg"
+				# cv2.imwrite(filename=filename, img=frame)
+				# image = face_recognition.load_image_file(filename)
+				# image = Image.fromarray(frame)
+				# image = image.convert('RGB')
+				face_encoding = face_recognition.face_encodings(frame)[0]
+				if ref_id in embed_dictt:
+					embed_dictt[ref_id]+=[face_encoding]
+				else:
+					embed_dictt[ref_id]=[face_encoding]
+				webcam.release()
+				# img_new = cv2.imread('saved_img.jpg', cv2.IMREAD_GRAYSCALE)
+				# img_new = cv2.imshow("Captured Image", img_new)
+				cv2.waitKey(1)
+				cv2.destroyAllWindows()     
+				break
+		elif key == ord('q'):
+			print("Turning off camera.")
+			webcam.release()
+			print("Camera off.")
+			print("Program ended.")
+			cv2.destroyAllWindows()
+			break
+f=open("ref_embed.pkl","wb")
+pickle.dump(embed_dictt,f)
+f.close()

examples/face-detection/recognition.py

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+import face_recognition
+import cv2
+import numpy as np
+import glob
+import time
+import csv
+import pickle
+# import mysql.connector
+# import datetime
+
+
+
+f=open("ref_name.pkl","rb")
+ref_dictt=pickle.load(f)         #ref_dict=ref vs name
+f.close()
+
+f=open("ref_embed.pkl","rb")
+embed_dictt=pickle.load(f)      #embed_dict- ref  vs embedding 
+f.close()
+
+############################################################################  encodings and ref_ids 
+known_face_encodings = []  #encodingd of faces
+known_face_names = []	   #ref_id of faces
+
+
+
+for ref_id , embed_list in embed_dictt.items():
+	for embed in embed_list:
+		known_face_encodings +=[embed]
+		known_face_names += [ref_id]
+
+
+
+#############################################################frame capturing from camera and face recognition
+video_capture = cv2.VideoCapture(0)
+# Initialize some variables
+face_locations = []
+face_encodings = []
+face_names = []
+process_this_frame = True
+
+while True  :
+	# Grab a single frame of video
+	ret, frame = video_capture.read()
+
+	# 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 = 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]
+			face_names.append(name)
+
+	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
+
+		              #updating in database
+
+		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)
+		font = cv2.FONT_HERSHEY_DUPLEX
+		cv2.putText(frame, ref_dictt[name], (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
+	font = cv2.FONT_HERSHEY_DUPLEX
+	# cv2.putText(frame, last_rec[0], (6,20), font, 1.0, (0,0 ,0), 1)
+
+	# Display the resulting imagecv2.putText(frame, ref_dictt[name], (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
+	cv2.imshow('Video', frame)
+
+	# Hit 'q' on the keyboard to quit!
+	if cv2.waitKey(1) & 0xFF == ord('q'):
+		# t.cancel()
+		break
+
+		# break
+
+# Release handle to the webcam
+video_capture.release()
+cv2.destroyAllWindows()