Merge pull request #605 from Tejaswi-Kumar/face_recognition

Added Real-time Face Recognition python script #603

Author: pawangeek

face_recognition/README.md

@@ -0,0 +1,26 @@
+# Detect face with openCV
+
+Simple script to recognize a human face from a given folder of similar human faces.
+
+# Python requirements
+
+Python 3.0 or newer
+
+## How to use it
+
+Make a folder in the folder where the python script "face_recognition.py" is there and name it as "faces".
+Then put the images of the people that the script needs to recognize in the "faces" folder.
+Before running the script, install the requirements of the script using the command:
+
+```
+pip install -r requirements.txt
+```
+
+## Setup and activate virtual environment :
+
+For Unix based systems please execute the following command to create venv and install requirements.
+
+```
+make init
+source .venv/bin/activate
+```

face_recognition/face_recognition.py

@@ -0,0 +1,79 @@
+import face_recognition as fr
+import os
+import cv2
+import numpy as np
+import time
+from VideoCapture import Device
+
+
+def get_encoded_faces():
+
+    encoded = {}
+
+    for dirpath, dnames, fnames in os.walk("./faces"):
+        for f in fnames:
+            if f.endswith(".jpg") or f.endswith(".png"):
+                face = fr.load_image_file("faces/" + f)
+                encoding = fr.face_encodings(face)[0]
+                encoded[f.split(".")[0]] = encoding
+
+    return encoded
+
+
+def unknown_image_encoded(img):
+
+    face = fr.load_image_file("faces/" + img)
+    encoding = fr.face_encodings(face)[0]
+
+    return encoding
+
+
+def classify_face(im):
+
+    faces = get_encoded_faces()
+    faces_encoded = list(faces.values())
+    known_face_names = list(faces.keys())
+    cam = Device()
+    cam.saveSnapshot('~/faces/image.jpg')
+    time.sleep(5)
+    img = cv2.imread(im, 1)
+    # img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
+    # img = img[:,:,::-1]
+
+    face_locations = fr.face_locations(img)
+    unknown_face_encodings = fr.face_encodings(img, face_locations)
+
+    face_names = []
+    for face_encoding in unknown_face_encodings:
+        # See if the face is a match for the known face(s)
+        matches = fr.compare_faces(faces_encoded, face_encoding)
+        name = "Unknown"
+
+        # use the known face with the smallest distance to the new face
+        face_distances = fr.face_distance(faces_encoded, 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)
+
+        for (top, right, bottom, left), name in zip(face_locations, face_names):
+            # Draw a box around the face
+            cv2.rectangle(img, (left - 20, top - 20),
+                          (right + 20, bottom + 20), (255, 0, 0), 2)
+
+            # Draw a label with a name below the face
+            cv2.rectangle(img, (left - 20, bottom - 15),
+                          (right + 20, bottom + 20), (255, 0, 0), cv2.FILLED)
+            font = cv2.FONT_HERSHEY_DUPLEX
+            cv2.putText(img, name, (left - 20, bottom + 15),
+                        font, 1.0, (255, 255, 255), 2)
+
+    # Display the resulting image
+    img1 = cv2.resize(img, (960, 540))
+    cv2.imshow('Result', img1)
+    cv2.waitKey(0)
+    return face_names
+
+
+print(classify_face("image.jpg"))

face_recognition/requirements.txt

@@ -0,0 +1,4 @@
+face_recognition
+cv2
+numpy
+VideoCapture