initial commit

README.md

@@ -1 +1,4 @@
-# face-recognition
+# face-recognition
+
+1. Run python3 train.py to augment dataset,train and save classifier.
+2. Run python3 face_recognition_cam.py to recognize faces.

face_rec.py

@@ -0,0 +1,121 @@
+import cv2
+import numpy as np
+import dlib
+import mediapipe as mp 
+import pickle
+from utils.align_face import align_face
+from  utils.encoding import get_encode
+RESIZE_HEIGHT = 240
+facemodel =  pickle.load(open('res/facemodel.pkl', 'rb'))
+celebs = np.load('res/people.npy',allow_pickle=True)
+
+
+cap = cv2.VideoCapture(0)
+detector = dlib.get_frontal_face_detector()
+pred_threshold = 0.55
+offset = 40
+
+mp_face_mesh = mp.solutions.face_mesh
+mp_drawing = mp.solutions.drawing_utils 
+mp_drawing_styles = mp.solutions.drawing_styles
+
+
+def findCenter(x,y,w,h):
+    cx = int((x+w)/2)
+    cy = int((y+h)/2)
+    return cx,cy
+
+def pointInRect(x,y,w,h,cx,cy):
+    x1, y1 = cx,cy
+    if (x < x1 and x1 < x+w):
+        if (y < y1 and y1 < y+h):
+            return True
+    else:
+        return False
+
+
+def recognize_face(cropped,face_mesh):
+    '''
+    takes in cropped face, aligns it, get 128D embedded vectors
+    and performs inference using the trained classifier. 
+
+    Args:
+
+    Returns: 'Unknown' if prediction is < 0.5 or returns name of person
+    '''
+    try:
+        faces = align_face(cropped,face_mesh)
+        embedding = get_encode(faces)
+        embedding = np.array(embedding).reshape(1,-1)
+        predict = facemodel.predict_proba(embedding)
+        who=np.max(predict)
+        if who < pred_threshold:
+            return 'unknown'
+        else:
+            who = facemodel.predict(embedding)[0]
+            return celebs[who]
+    except:
+      return
+
+
+identified = []
+count=0
+SKIP_FRAMES=2
+trackers=[]
+with mp_face_mesh.FaceMesh(
+        static_image_mode=False,
+        refine_landmarks=True,
+        max_num_faces=1,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,) as face_mesh:
+
+        while True:
+
+            ret, frame_normal = cap.read()
+            frame_copy = frame_normal.copy()
+            height, width, c = frame_normal.shape
+
+            DOWNSAMPLE_RATIO = int(height/RESIZE_HEIGHT)
+            img = cv2.resize(frame_normal,None,
+                                fx=1.0/DOWNSAMPLE_RATIO, 
+                                fy=1.0/DOWNSAMPLE_RATIO, 
+                                interpolation = cv2.INTER_LINEAR)
+
+
+
+            dets = detector(img)
+            for i, d in enumerate(dets):
+                cv2.rectangle(img,(d.left(),d.top()),(d.right(),d.bottom()),(0,0,255,2))
+                x,y = d.left()*DOWNSAMPLE_RATIO,d.top()*DOWNSAMPLE_RATIO
+                w,h = d.right()*DOWNSAMPLE_RATIO,d.bottom()*DOWNSAMPLE_RATIO
+                cropped_image = frame_copy[y-offset:h+offset,
+                                            x-offset:w+offset]
+
+                pt_1,pt_2 = (x,y),(w,h)
+                cv2.imshow(f'frame_cropped_{i}',cropped_image)
+                pred_name = recognize_face(cropped_image,face_mesh)
+                if pred_name=='unknown':
+                    cv2.rectangle(frame_normal, pt_1, pt_2, (0, 0, 255), 2)
+                    cv2.putText(frame_normal, pred_name, pt_1, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+                elif pred_name==None:
+                    pass
+                else:
+                    cv2.rectangle(frame_normal, pt_1, pt_2, (0, 255, 0), 2)
+                    cv2.putText(frame_normal, pred_name, pt_1, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) 
+
+
+                    # Display the resulting image
+            cv2.imshow('Video', frame_normal)
+
+            # Hit 'q' on the keyboard to quit!
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+# Release handle to the webcam
+cap.release()
+cv2.destroyAllWindows()
+
+
+
+

face_recognition_cam.py

@@ -0,0 +1,168 @@
+import cv2
+import numpy as np
+import dlib
+import mediapipe as mp 
+import pickle
+from utils.align_face import align_face
+from  utils.encoding import get_encode
+RESIZE_HEIGHT = 240
+facemodel =  pickle.load(open('res/facemodel.pkl', 'rb'))
+celebs = np.load('res/people.npy',allow_pickle=True)
+
+
+cap = cv2.VideoCapture(0)
+detector = dlib.get_frontal_face_detector()
+pred_threshold = 0.55
+offset = 40
+
+mp_face_mesh = mp.solutions.face_mesh
+mp_drawing = mp.solutions.drawing_utils 
+mp_drawing_styles = mp.solutions.drawing_styles
+
+
+def findCenter(x,y,w,h):
+    cx = int((x+w)/2)
+    cy = int((y+h)/2)
+    return cx,cy
+
+def pointInRect(x,y,w,h,cx,cy):
+    x1, y1 = cx,cy
+    if (x < x1 and x1 < x+w):
+        if (y < y1 and y1 < y+h):
+            return True
+    else:
+        return False
+
+
+def recognize_face(cropped,face_mesh):
+    '''
+    takes in cropped face, aligns it, get 128D embedded vectors
+    and performs inference using the trained classifier. 
+
+    Args:
+
+    Returns: 'Unknown' if prediction is < 0.5 or returns name of person
+    '''
+    try:
+        faces = align_face(cropped,face_mesh)
+        embedding = get_encode(faces)
+        embedding = np.array(embedding).reshape(1,-1)
+        predict = facemodel.predict_proba(embedding)
+        who=np.max(predict)
+        if who < pred_threshold:
+            return 'unknown'
+        else:
+            who = facemodel.predict(embedding)[0]
+            return celebs[who]
+    except:
+      return
+
+
+# identified = {}
+count=0
+SKIP_FRAMES=2
+trackers=[]
+with mp_face_mesh.FaceMesh(
+        static_image_mode=False,
+        refine_landmarks=True,
+        max_num_faces=1,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,) as face_mesh:
+
+        while True:
+
+            ret, frame_normal = cap.read()
+            frame_copy = frame_normal.copy()
+            height, width, c = frame_normal.shape
+
+            DOWNSAMPLE_RATIO = int(height/RESIZE_HEIGHT)
+            img = cv2.resize(frame_normal,None,
+                                fx=1.0/DOWNSAMPLE_RATIO, 
+                                fy=1.0/DOWNSAMPLE_RATIO, 
+                                interpolation = cv2.INTER_LINEAR)
+
+
+            trackers_to_del=[]
+            for tid,trackersid in enumerate(trackers):
+                trackingQuality = trackersid[0].update(frame_normal)
+                if trackingQuality < 5:
+                    trackers_to_del.append(trackersid[0])
+            try:
+                for trackersid in trackers_to_del:
+                    trackers.pop(tid)
+            except IndexError:
+                pass
+            # Detector is ran after every 10 frames
+            if (count % SKIP_FRAMES) == 0:
+                dets = detector(img)
+                for i, d in enumerate(dets):
+                    cv2.rectangle(img,(d.left(),d.top()),(d.right(),d.bottom()),(0,0,255,2))
+                    x,y = d.left()*DOWNSAMPLE_RATIO,d.top()*DOWNSAMPLE_RATIO
+                    w,h = d.right()*DOWNSAMPLE_RATIO,d.bottom()*DOWNSAMPLE_RATIO
+                    cropped_image = frame_copy[y-offset:h+offset,
+                                                x-offset:w+offset]
+
+                    pt_1,pt_2 = (x,y),(w,h)
+                    # cv2.imshow(f'{i}_frame_cropped',cropped_image)
+                    pred_name = recognize_face(cropped_image,face_mesh)
+                    if pred_name=='unknown':
+                        cv2.rectangle(frame_normal, pt_1, pt_2, (0, 0, 255), 2)
+                        cv2.putText(frame_normal, pred_name, pt_1, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+                    elif pred_name==None:
+                        continue
+                    else:
+                        cv2.rectangle(frame_normal, pt_1, pt_2, (0, 255, 0), 2)
+                        cv2.putText(frame_normal, pred_name, pt_1, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) 
+
+
+                    rect = dlib.rectangle(x,y,w,h)
+                    tracking = False
+
+                    for trackersid in trackers:
+                        pos = trackersid[0].get_position()
+                        startX = int(pos.left())
+                        startY = int(pos.top())
+                        endX = int(pos.right())
+                        endY = int(pos.bottom())
+                        tx,ty=findCenter(startX,startY,endX,endY)
+                        t_location_chk = pointInRect(x,y,w,h,tx,ty)
+                        if t_location_chk:
+                            tracking = True
+                            # identified.update({pred_name: (startX,startY)}) 
+
+                    if not tracking :
+                        tracker = dlib.correlation_tracker()
+                        tracker.start_track(frame_normal, rect)
+                        trackers.append([tracker,pred_name,frame_normal])
+
+            for num,trackersid in enumerate(trackers):
+                pos = trackersid[0].get_position()
+                startX = int(pos.left())
+                startY = int(pos.top())
+                endX = int(pos.right())
+                endY = int(pos.bottom())
+                # offset=0
+                cv2.rectangle(frame_normal, (startX, startY),
+                                (endX, endY),(0, 255, 250), 3)
+                cv2.putText(frame_normal, trackersid[1], (startX,startY), cv2.FONT_HERSHEY_SIMPLEX, 1, (250, 0, 255), 2)
+
+
+            count+=1
+            if count==11:
+                count=0
+
+            # Display output
+            cv2.imshow('Video', frame_normal)
+
+            # Hit 'q' on the keyboard to quit!
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+# Release handle to the webcam
+cap.release()
+cv2.destroyAllWindows()
+
+
+
+