face recognition with 4 images

.gitignore

@@ -56,3 +56,9 @@ alpha_nlu/aclImdb/
 alpha_nlu/aclImdb_v1.tar.gz
 alpha_nlu/metadata.tsv
 alpha_nlu/vectors.tsv
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+
+# virtual env
+env/

epsilon_cv/face_comparison.py

@@ -0,0 +1,43 @@
+"""
+General description
+TODO : add MIT liscence
+
+"""
+
+# nom de fonction : ma_fonction()
+# variable : ma_variable
+# nom de classe : MaClasse()
+
+import cv2
+from simple_facerec import SimpleFacerec
+
+# Encode faces from a folder
+sfr = SimpleFacerec()
+sfr.load_encoding_images("epsilon_cv/images")
+
+
+# Load Camera
+cap = cv2.VideoCapture(0)
+
+if not (cap.isOpened()):
+    print("Could not open video device")
+
+while True:
+    ret, frame = cap.read()
+
+    # Detect Faces
+    face_locations, face_names = sfr.detect_known_faces(frame)
+    for face_loc, name in zip(face_locations, face_names):
+        y1, x2, y2, x1 = face_loc[0], face_loc[1], face_loc[2], face_loc[3]
+
+        cv2.putText(frame, name,(x1, y1 - 10), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 200), 2)
+        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 200), 4)
+
+    cv2.imshow("Frame", frame)
+
+    key = cv2.waitKey(1)
+    if key == 27:  # escape button to quit
+        break
+
+cap.release()
+cv2.destroyAllWindows()

epsilon_cv/simple_facerec.py

@@ -0,0 +1,74 @@
+# Source: pysource.com
+# https://pysource.com/2021/08/16/face-recognition-in-real-time-with-opencv-and-python/
+
+import face_recognition
+import cv2
+import os
+import glob
+import numpy as np
+
+class SimpleFacerec:
+    def __init__(self):
+        self.known_face_encodings = []
+        self.known_face_names = []
+
+        # Resize frame for a faster speed
+        self.frame_resizing = 0.25
+
+    def load_encoding_images(self, images_path):
+        """
+        Load encoding images from path
+        :param images_path:
+        :return:
+        """
+        # Load Images
+        images_path = glob.glob(os.path.join(images_path, "*.*"))
+
+        print("{} encoding images found.".format(len(images_path)))
+
+        # Store image encoding and names
+        for img_path in images_path:
+            img = cv2.imread(img_path)
+            rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+            # Get the filename only from the initial file path.
+            basename = os.path.basename(img_path)
+            (filename, ext) = os.path.splitext(basename)
+            # Get encoding
+            img_encoding = face_recognition.face_encodings(rgb_img)[0]
+
+            # Store file name and file encoding
+            self.known_face_encodings.append(img_encoding)
+            self.known_face_names.append(filename)
+        print("Encoding images loaded")
+
+    def detect_known_faces(self, frame):
+        small_frame = cv2.resize(frame, (0, 0), fx=self.frame_resizing, fy=self.frame_resizing)
+        # Find all the faces and face encodings in the current frame of video
+        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
+        rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
+        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(self.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(self.known_face_encodings, face_encoding)
+            best_match_index = np.argmin(face_distances)
+            if matches[best_match_index]:
+                name = self.known_face_names[best_match_index]
+            face_names.append(name)
+
+        # Convert to numpy array to adjust coordinates with frame resizing quickly
+        face_locations = np.array(face_locations)
+        face_locations = face_locations / self.frame_resizing
+        return face_locations.astype(int), face_names

requirements.txt

@@ -0,0 +1,2 @@
+cv2
+face_recognition