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multi_face.py

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+# -*- coding: utf-8 -*-
+"""multi_face.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/165AXrMIl0tEIwez9nDZSTL9QIg7mo5aH
+"""
+
+# import the necessary packages
+from imutils import paths
+import face_recognition
+import argparse
+import pickle
+import cv2
+import os
+
+pip install face_recognition
+
+os.chdir('drive/My Drive/dataset2')
+
+# grab the paths to the input images in our dataset
+print("[INFO] quantifying faces...")
+imagePaths = list(paths.list_images('data'))
+# initialize the list of known encodings and known names
+knownEncodings = []
+knownNames = []
+
+args = []
+args["detection_method"] = "cnn"
+
+# loop over the image paths
+for (i, imagePath) in enumerate(imagePaths):
+	# extract the person name from the image path
+	print("[INFO] processing image {}/{}".format(i + 1,
+		len(imagePaths)))
+	name = imagePath.split(os.path.sep)[-2]
+	# load the input image and convert it from BGR (OpenCV ordering)
+	# to dlib ordering (RGB)
+	image = cv2.imread(imagePath)
+	rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+ 	# detect the (x, y)-coordinates of the bounding boxes
+	# corresponding to each face in the input image
+	boxes = face_recognition.face_locations(rgb,
+		model="cnn")
+	# compute the facial embedding for the face
+	encodings = face_recognition.face_encodings(rgb, boxes)
+	# loop over the encodings
+	for encoding in encodings:
+		# add each encoding + name to our set of known names and
+		# encodings
+		knownEncodings.append(encoding)
+		knownNames.append(name)
+
+# dump the facial encodings + names to disk
+print("[INFO] serializing encodings...")
+data = {"encodings": knownEncodings, "names": knownNames}
+f = open("encodings1.pickle", "wb")
+f.write(pickle.dumps(data))
+f.close()
+
+ls
+
+"""## **Training Completed Now Testing**
+
+ ---
+"""
+
+os.chdir('drive/My Drive/dataset2') # This is where my dataset is placed
+
+ls
+
+# import the necessary packages
+import face_recognition
+import argparse
+import pickle
+import cv2
+
+encodings = "encodings1.pickle"
+images = "photo2.jpg"
+detection_method = "cnn"
+
+# load the known faces and embeddings
+print("[INFO] loading encodings...")
+data = pickle.loads(open(encodings, "rb").read())
+# load the input image and convert it from BGR to RGB
+image = cv2.imread(images)
+rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+# detect the (x, y)-coordinates of the bounding boxes corresponding
+# to each face in the input image, then compute the facial embeddings
+# for each face
+print("[INFO] recognizing faces...")
+boxes = face_recognition.face_locations(rgb,
+	model=detection_method)
+encodings = face_recognition.face_encodings(rgb, boxes)
+# initialize the list of names for each face detected
+names = []
+
+class Actor:   
+    # __init__ function 
+    def __init__(self,name,age,gender,born): 
+        self.name = name
+        self.age = "("+str(age)+")"
+        self.gender =gender
+        self.born =born
+
+downey = Actor("Downey",55,"MALE","New York")
+chris = Actor("Chris",36,"MALE","Melbourne")
+jeremy = Actor("Jeremy",49,"MALE","California")
+scarlett = Actor("Scarlett",35,"FEMALE","New York")
+
+eval("downey").name
+
+# loop over the facial embeddings
+for encoding in encodings:
+	# attempt to match each face in the input image to our known
+	# encodings
+	matches = face_recognition.compare_faces(data["encodings"],
+		encoding)
+	name = "Unknown"
+  	# check to see if we have found a match
+	if True in matches:
+		# find the indexes of all matched faces then initialize a
+		# dictionary to count the total number of times each face
+		# was matched
+		matchedIdxs = [i for (i, b) in enumerate(matches) if b]
+		counts = {}
+		# loop over the matched indexes and maintain a count for
+		# each recognized face face
+		for i in matchedIdxs:
+			name = data["names"][i]
+			counts[name] = counts.get(name, 0) + 1
+		# determine the recognized face with the largest number of
+		# votes (note: in the event of an unlikely tie Python will
+		# select first entry in the dictionary)
+		name = max(counts, key=counts.get)
+
+	# update the list of names
+	names.append(name)
+
+# loop over the recognized faces
+for ((top, right, bottom, left), name) in zip(boxes, names):
+	# draw the predicted face name on the image
+  if(name != "Unknown"):
+    name = eval(name)
+    cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
+    y = top - 15 if top - 15 > 15 else top + 15
+    cv2.putText(image, name.name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 191, 0), 2)
+    cv2.putText(image, name.age, (left+90, y), cv2.FONT_HERSHEY_SIMPLEX,0.5, (0, 0, 255), 2)
+    # cv2.putText(image, name.gender, (left+90, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 191, 0), 2)
+    # cv2.putText(image, name.born, (left+80, y), cv2.FONT_HERSHEY_SIMPLEX,0.5, (0, 191, 0), 2)
+
+# Commented out IPython magic to ensure Python compatibility.
+# %pylab inline
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+# img=mpimg.imread('your_image.png')
+imgplot = plt.imshow(image)
+plt.show()
+
+from google.colab.patches import cv2_imshow
+
+cv2_imshow(image)
+
+"""##Database Can be seen below"""
+
+os.chdir('data')
+
+ls
+
+#Four Directory with People Names