sellenth · Korux · May 6, 2020 · May 6, 2020 · May 8, 2020 · May 8, 2020
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diff --git a/...ion/data/val/mindy_kaling/httpcdnpastemagazinecomwwwarticlesmindyprojectjpg.jpg b/...ion/data/val/mindy_kaling/httpcdnpastemagazinecomwwwarticlesmindyprojectjpg.jpg
diff --git a/...val/mindy_kaling/httpdbrbzkkbdsdcloudfrontnetwpcontentuploadsMindyKalingjpg.jpg b/...val/mindy_kaling/httpdbrbzkkbdsdcloudfrontnetwpcontentuploadsMindyKalingjpg.jpg
diff --git a/face-recognition/data/val/steve_jobs/aa.jpg b/face-recognition/data/val/steve_jobs/aa.jpg
diff --git a/face-recognition/face_embeds_dataset.npz b/face-recognition/face_embeds_dataset.npz
diff --git a/face-recognition/face_recog.py b/face-recognition/face_recog.py
@@ -0,0 +1,87 @@
+import os
+import os.path
+import numpy as np
+from numpy import expand_dims
+from keras.models import load_model
+from mtcnn import MTCNN
+import cv2
+
+def get_face(file):
+    IMAGE_SIZE = (160,160)
+    detector = MTCNN()
+    try:
+        img = cv2.cvtColor(cv2.imread(file),cv2.COLOR_BGR2RGB)
+        faces = detector.detect_faces(img)
+        if len(faces) < 1:
+            print('No faces detected')
+            return None
+        x,y,width,height = faces[0]["box"]
+        cropped = cv2.resize(img[y:y+height,x:x+width],IMAGE_SIZE)
+        return cropped
+    except:
+        return None
+
+
+
+def get_face_frame(frame):
+    detector = MTCNN()
+    faces = detector.detect_faces(frame)
+    if len(faces) < 1:
+        print('No faces detected')
+        return None
+    return faces
+
+def format_face_frame(faces,frame):
+    IMAGE_SIZE = (160,160)
+    x,y,width,height = faces[0]["box"]
+    cropped = cv2.resize(frame[y:y+height,x:x+width],IMAGE_SIZE)
+    return cropped
+
+#standardize pixel values
+
+def prewhiten(x):
+    if x.ndim == 4:
+        axis = (1, 2, 3)
+        size = x[0].size
+    elif x.ndim == 3:
+        axis = (0, 1, 2)
+        size = x.size
+    else:
+        raise ValueError('Dimension should be 3 or 4')
+
+    mean = np.mean(x, axis=axis, keepdims=True)
+    std = np.std(x, axis=axis, keepdims=True)
+    std_adj = np.maximum(std, 1.0/np.sqrt(size))
+    y = (x - mean) / std_adj
+    return y
+
+#normalize embedding vectors
+
+def l2_normalize(x, axis=-1, epsilon=1e-10):
+    output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
+    return output
+
+#returns a list of embeddings from the list of images passed in
+#list of images must be of type np.ndarray
+#model is loaded from facenet_keras.h5
+
+def get_embeddings(imgs,model):
+    imgs = prewhiten(imgs)
+    raws = []
+    for raw in imgs:
+        img = expand_dims(raw,axis=0)
+        raws.append(model.predict(img))
+    embeds = l2_normalize(np.concatenate(raws))
+    return embeds
+
+#return the distance between 2 embeddings
+
+def get_euclidean_distance(emb1,emb2):
+   return np.linalg.norm(emb1-emb2)
+
+def embed_to_string(embed):
+    return ",".join([str(i) for i in list(embed)])
+
+def string_to_embed(string):
+    embed = np.array(string.split(","))
+    return embed.astype(np.float)
diff --git a/face-recognition/face_register.py b/face-recognition/face_register.py
@@ -0,0 +1,72 @@
+import tkinter as tk
+import threading
+import numpy as np
+import cv2
+from tkinter import filedialog
+from keras.models import load_model
+from face_recog import *
+
+def register_face():
+
+    cap = cv2.VideoCapture(0)
+    if cap is None or not cap.isOpened():
+        user_in = input("This device does not have a camera. Do you want to submit an image file instead?(y/n): ")
+        while user_in.lower() != "y" and user_in.lower() != "n":
+            user_in = input("This device does not have a camera. Do you want to submit an image file instead?(y/n): ")
+        if user_in == "n":
+            print("Error: could not get user's face, exiting...")
+            return None
+    else:
+        face = None
+        while cap.isOpened():
+            input("Look directly into the camera and press any button to take a picture...")
+            ret,frame = cap.read()
+            if ret == True:
+                face = get_face_frame(frame)
+                if face is not None:
+                    face = format_face_frame(face,frame)
+                    break
+                else:
+                    user_in = None
+                    while user_in.lower() != "y" and user_in.lower() != "n":
+                        user_in = input("No faces detected, try again? (y/n): ")
+                    if user_in == "n":
+                        print("Error: could not get user's face, exiting...")
+                        return None
+            else:
+                user_in = None
+                while user_in.lower() != "y" and user_in.lower() != "n":
+                    user_in = input("No faces detected, try again? (y/n): ")
+                if user_in == "n":
+                    print("Error: could not get user's face, exiting...")
+                    return None
+        print("Found user's face, saving... (may take up to 1 minute)")
+        model = load_model("./facenet_keras.h5")
+        embed = get_embeddings(np.array([face]),model)
+        cap.release()
+        cv2.destroyAllWindows()
+        print("Successfully saved! exiting...")
+        return embed[0]
+
+
+    root = tk.Tk()
+    root.wm_attributes('-topmost',1)
+    root.withdraw()
+
+    face = None
+    while True:
+        file_path = filedialog.askopenfilename()
+        if file_path == "": break
+        face = get_face(file_path)
+        if face is not None: break
+        else: print("No face found, please try again.")
+    if face is None:
+        print("Error: could not get user's face, exiting...")
+        return None
+    else:
+        print("Found user's face, saving... (may take up to 1 minute)")
+        model = load_model("./facenet_keras.h5")
+        embed = get_embeddings(np.array([face]),model)
+        print("Successfully saved! exiting...")
+        return embed[0]
+
diff --git a/face-recognition/face_submit.py b/face-recognition/face_submit.py
@@ -0,0 +1,83 @@
+import cv2
+import tkinter as tk
+from tkinter import filedialog
+from face_recog import *
+from keras.models import load_model
+
+# allows submitting a face through file upload
+# for demonstration purposes only, should not be used in final build
+
+def submit_face_tmp():
+    root = tk.Tk()
+    root.wm_attributes('-topmost',1)
+    root.withdraw()
+
+    face = None
+    while True:
+        file_path = filedialog.askopenfilename()
+        if file_path == "": break
+        face = get_face(file_path)
+        if face is not None: break
+        else: print("No face found, please try again.")
+    if face is None:
+        print("Error: could not get user's face, exiting...")
+        return None
+    else:
+        return face
+
+# grab the user's face using screen capture
+
+def get_face_from_camera(cap):
+
+    c = -1
+    face = None
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        if ret==True:
+            cv2.imshow('frame',frame)
+            if c == 20 or c == -1:
+                face = get_face_frame(frame)
+                if face is not None:
+                    face = format_face_frame(face,frame)
+                    break
+                c = 0
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+            c+=1
+
+        else:
+            break
+
+    # Release everything if job is finished
+    cap.release()
+    cv2.destroyAllWindows()
+    return face
+
+def submit_face():
+    cap = cv2.VideoCapture(0)
+    if cap is None or not cap.isOpened():
+        face = submit_face_tmp()
+        if face is None:
+            print("Error: could not get user's face, exiting...")
+            return None
+        else:
+            print("Found user's face, saving... (may take up to 1 minute)")
+            model = load_model("./facenet_keras.h5")
+            embed = get_embeddings(np.array([face]),model)
+            print("Successfully saved! exiting...")
+            return embed[0]
+        # print("This node does not have a camera, exiting...")
+        # return None
+    input("Look directly at the camera. (Press any key to continue)")
+    print("Searching for face... (press Q to quit")
+    face = get_face_from_camera(cap)
+    if face is None:
+        print("Error: could not get user's face, exiting...")
+        return None
+    print("Found user's face, submitting... (may take up to 1 minute)")
+    model = load_model("./facenet_keras.h5")
+    embed = get_embeddings(np.array([face]),model)
+    print("Successfully submitted! exiting...")
+    return embed[0]
+
+