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betabot-vision
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Initial commit

James Peret 9 ans auparavant
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99e640f749
11 fichiers modifiés avec 2797 ajouts et 0 suppressions
Afficher les stats Diff
  1. BIN
      .DS_Store
  2. 2 0
      .gitignore
  3. 124 0
      camera_test.py
  4. 122 0
      camera_test_v3.py
  5. 132 0
      camera_test_v4.py
  6. 129 0
      face_camera_v2.py
  7. 217 0
      face_recognizer_v1.py
  8. 220 0
      face_recognizer_v2.py
  9. 224 0
      face_recognizer_v3.py
  10. 122 0
      facetracker.py
  11. 1505 0
      lbpcascade_frontalface.xml

BIN
.DS_Store
Voir le fichier


+ 2 - 0
.gitignore
Voir le fichier 

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1 
+faces.csv
2 
+photos/

+ 124 - 0
camera_test.py
Voir le fichier 

@@ -0,0 +1,124 @@
1 
+import Tkinter as tk
2 
+import cv2, sys, time, os, math
3 
+from PIL import Image, ImageTk
4 
+import numpy as numpy
5 
+
6 
+from os import listdir
7 
+from os.path import isfile, join
8 
+
9 
+# Load the BCM V4l2 driver for /dev/video0
10 
+os.system('sudo modprobe bcm2835-v4l2')
11 
+# Set the framerate ( not sure this does anything! )
12 
+os.system('v4l2-ctl -p 4')
13 
+
14 
+width, height = 320, 240
15 
+cap = cv2.VideoCapture(0)
16 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
17 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
18 
+
19 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
20 
+faceCascade = cv2.CascadeClassifier(cascPath)
21 
+
22 
+root = tk.Tk()
23 
+root.attributes("-fullscreen", True)
24 
+root.bind('<Escape>', lambda e: root.quit())
25 
+
26 
+lmain = tk.Label(root)
27 
+lmain.pack()
28 
+
29 
+last_image_faces = []
30 
+
31 
+def show_frame():
32 
+    _, frame = cap.read()
33 
+    frame = cv2.flip(frame, 1)
34 
+    frame = faceDetect(frame)
35 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
36 
+    img = Image.fromarray(cv2image)
37 
+    imgtk = ImageTk.PhotoImage(image=img)
38 
+    lmain.imgtk = imgtk
39 
+    lmain.configure(image=imgtk)
40 
+    lmain.after(1, show_frame)
41 
+
42 
+def faceDetect(frame):
43 
+
44 
+    # Do face detection
45 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
46 
+
47 
+    #Slower method
48 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
49 
+    gray = cv2.equalizeHist( gray )
50 
+    faces = faceCascade.detectMultiScale(
51 
+	gray,
52 
+        scaleFactor=1.1,
53 
+        minNeighbors=4,
54 
+        minSize=(20, 20),
55 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
56 
+    )
57 
+
58 
+    print "Found {0} faces!".format(len(faces))
59 
+    global last_image_faces
60 
+    image_faces = []
61 
+
62 
+    for (x, y, w, h) in faces:
63 
+        # Draw a green rectangle around the face
64 
+        face = frame[y:(y+h), x:(x+w)]
65 
+        center_x = x + (w/2)
66 
+        center_y = y + (h/2)
67 
+        center = [center_x, center_y]
68 
+        image_faces.append(center)
69 
+        tracking = False
70 
+        for pos in last_image_faces:
71 
+            #dist = sqrt( (center_x - pos[0])**2 + (center_y - pos[1])**2 )
72 
+            dist = math.hypot(center_x - pos[0], center_y - pos[1])
73 
+            print("Distance from last point " + str(dist))
74 
+            if dist < 30:
75 
+                tracking = True
76 
+        if tracking == False:
77 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
78 
+            recognizeFace(face)
79 
+        else:
80 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
81 
+        break
82 
+    last_image_faces = image_faces
83 
+    return frame
84 
+
85 
+def recognizeFace(face):
86 
+    print("Searching Face database...")
87 
+    face = cv2.cvtColor(face, cv2.COLOR_BGR2RGBA)
88 
+    count = 0
89 
+    match_found = False
90 
+    for f in face_db:
91 
+        count = count + 1
92 
+        # Initiate SIFT detector
93 
+        orb = cv2.ORB()
94 
+        # find the keypoints and descriptors with SIFT
95 
+        kp1, des1 = orb.detectAndCompute(face,None)
96 
+        kp2, des2 = orb.detectAndCompute(f,None)
97 
+        # create BFMatcher object
98 
+        bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
99 
+        # Match descriptors.
100 
+        matches = bf.match(des1,des2)
101 
+        if len(matches) > 0:
102 
+            # found match
103 
+            print("Match Found! (" + str(count) +")")
104 
+            match_found = True
105 
+            break
106 
+    if match_found == False:
107 
+        # Save picture
108 
+        print("No match found! Searched " + str(count) + " records. Saving image.")
109 
+        cv2.imwrite("/home/pi/photos/faces/face-" + str(len(face_db)) + ".jpg", face)
110 
+        loadFaceDB()
111 
+
112 
+def loadFaceDB():
113 
+    # Load faces
114 
+    face_db_path='/home/pi/photos/faces'
115 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
116 
+    global face_db
117 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
118 
+    for n in range(0, len(onlyfiles)):
119 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
120 
+
121 
+
122 
+loadFaceDB()
123 
+show_frame()
124 
+root.mainloop()

+ 122 - 0
camera_test_v3.py
Voir le fichier 

@@ -0,0 +1,122 @@
1 
+import Tkinter as tk
2 
+import cv2, sys, time, os, math
3 
+from PIL import Image, ImageTk
4 
+import numpy as numpy
5 
+
6 
+
7 
+from SimpleCV import Color, np
8 
+from SimpleCV import Image as SimpleImage
9 
+
10 
+from os import listdir
11 
+from os.path import isfile, join
12 
+
13 
+# Load the BCM V4l2 driver for /dev/video0
14 
+os.system('sudo modprobe bcm2835-v4l2')
15 
+# Set the framerate ( not sure this does anything! )
16 
+os.system('v4l2-ctl -p 4')
17 
+
18 
+width, height = 320, 240
19 
+cap = cv2.VideoCapture(0)
20 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
21 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
22 
+
23 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
24 
+faceCascade = cv2.CascadeClassifier(cascPath)
25 
+
26 
+root = tk.Tk()
27 
+root.attributes("-fullscreen", True)
28 
+root.bind('<Escape>', lambda e: root.quit())
29 
+
30 
+lmain = tk.Label(root)
31 
+lmain.pack()
32 
+
33 
+last_image_faces = []
34 
+
35 
+def show_frame():
36 
+    _, frame = cap.read()
37 
+    frame = cv2.flip(frame, 1)
38 
+    frame = faceDetect(frame)
39 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
40 
+    img = Image.fromarray(cv2image)
41 
+    imgtk = ImageTk.PhotoImage(image=img)
42 
+    lmain.imgtk = imgtk
43 
+    lmain.configure(image=imgtk)
44 
+    lmain.after(1, show_frame)
45 
+
46 
+def faceDetect(frame):
47 
+
48 
+    # Do face detection
49 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
50 
+
51 
+    #Slower method
52 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
53 
+    gray = cv2.equalizeHist( gray )
54 
+    faces = faceCascade.detectMultiScale(
55 
+	gray,
56 
+        scaleFactor=1.1,
57 
+        minNeighbors=4,
58 
+        minSize=(20, 20),
59 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
60 
+    )
61 
+
62 
+    print "Found {0} faces!".format(len(faces))
63 
+    global last_image_faces
64 
+    image_faces = []
65 
+
66 
+    for (x, y, w, h) in faces:
67 
+        # Draw a green rectangle around the face
68 
+        face = frame[y:(y+h), x:(x+w)]
69 
+        center_x = x + (w/2)
70 
+        center_y = y + (h/2)
71 
+        center = [center_x, center_y]
72 
+        image_faces.append(center)
73 
+        tracking = False
74 
+        for pos in last_image_faces:
75 
+            #dist = sqrt( (center_x - pos[0])**2 + (center_y - pos[1])**2 )
76 
+            dist = math.hypot(center_x - pos[0], center_y - pos[1])
77 
+            print("Distance from last point " + str(dist))
78 
+            if dist < 30:
79 
+                tracking = True
80 
+        if tracking == False:
81 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
82 
+            recognizeFace(face)
83 
+        else:
84 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
85 
+        break
86 
+    last_image_faces = image_faces
87 
+    return frame
88 
+
89 
+def recognizeFace(face):
90 
+    face = cv2.cvtColor(face, cv2.COLOR_BGR2RGBA)
91 
+    count = 0
92 
+    match_found = False
93 
+    for f in face_db:
94 
+        count = count + 1
95 
+        template = SimpleImage(f)
96 
+        cv_face = SimpleImage(face)
97 
+        print("Scaning DB face " + str(count))
98 
+        keypoints = cv_face.drawSIFTKeyPointMatch(template,distance=50)
99 
+        if keypoints:
100 
+            # found match
101 
+            print("Match Found! (" + str(count) +")")
102 
+            match_found = True
103 
+            break
104 
+    if match_found == False:
105 
+        # Save picture
106 
+        print("No match found! Searched " + str(count) + " records. Saving image.")
107 
+        cv2.imwrite("/home/pi/photos/faces/face-" + str(len(face_db)) + ".jpg", face)
108 
+        loadFaceDB()
109 
+
110 
+def loadFaceDB():
111 
+    # Load faces
112 
+    face_db_path='/home/pi/photos/faces'
113 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
114 
+    global face_db
115 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
116 
+    for n in range(0, len(onlyfiles)):
117 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
118 
+
119 
+
120 
+loadFaceDB()
121 
+show_frame()
122 
+root.mainloop()

+ 132 - 0
camera_test_v4.py
Voir le fichier 

@@ -0,0 +1,132 @@
1 
+# FACE CAMERA
2 
+
3 
+import Tkinter as tk
4 
+import cv2, sys, time, os, math
5 
+from PIL import Image, ImageTk
6 
+import numpy as numpy
7 
+
8 
+from os import listdir
9 
+from os.path import isfile, join
10 
+
11 
+import RPi.GPIO as GPIO
12 
+
13 
+# Load the BCM V4l2 driver for /dev/video0
14 
+os.system('sudo modprobe bcm2835-v4l2')
15 
+# Set the framerate ( not sure this does anything! )
16 
+os.system('v4l2-ctl -p 4')
17 
+
18 
+width, height = 320, 240
19 
+cap = cv2.VideoCapture(0)
20 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
21 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
22 
+
23 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
24 
+faceCascade = cv2.CascadeClassifier(cascPath)
25 
+
26 
+root = tk.Tk()
27 
+root.attributes("-fullscreen", True)
28 
+root.bind('<Escape>', lambda e: root.quit())
29 
+
30 
+lmain = tk.Label(root)
31 
+lmain.pack()
32 
+
33 
+last_image_faces = []
34 
+saved = False
35 
+
36 
+GPIO.setmode(GPIO.BOARD)
37 
+GPIO.setup(12, GPIO.IN, pull_up_down = GPIO.PUD_UP)
38 
+GPIO.setup(16, GPIO.IN, pull_up_down = GPIO.PUD_UP)
39 
+GPIO.setup(18, GPIO.IN, pull_up_down = GPIO.PUD_UP)
40 
+
41 
+
42 
+
43 
+def show_frame():
44 
+    _, frame = cap.read()
45 
+    frame = cv2.flip(frame, 1)
46 
+    frame = faceDetect(frame)
47 
+    #frame = cv2.resize(frame, (320,240))
48 
+    buttonPress()
49 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
50 
+    img = Image.fromarray(cv2image)
51 
+    imgtk = ImageTk.PhotoImage(image=img)
52 
+    lmain.imgtk = imgtk
53 
+    lmain.configure(image=imgtk)
54 
+    lmain.after(1, show_frame)
55 
+
56 
+def faceDetect(frame):
57 
+
58 
+    # Do face detection
59 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
60 
+
61 
+    #Slower method
62 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
63 
+    gray = cv2.equalizeHist( gray )
64 
+    faces = faceCascade.detectMultiScale(
65 
+	gray,
66 
+        scaleFactor=1.1,
67 
+        minNeighbors=4,
68 
+        minSize=(20, 20),
69 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
70 
+    )
71 
+
72 
+    #print "Found {0} faces!".format(len(faces))
73 
+    global last_image_faces
74 
+    image_faces = []
75 
+
76 
+    for (x, y, w, h) in faces:
77 
+        center_x = x + (w/2)
78 
+        center_y = y + (h/2)
79 
+        start_y = center_y - 80
80 
+        start_x = center_x - 80
81 
+        face_crop = frame[start_y:(start_y+160), start_x:(start_x+160)]
82 
+        image_faces.append(face_crop)
83 
+        # Draw a green rectangle around the face
84 
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
85 
+    last_image_faces = image_faces
86 
+    return frame
87 
+
88 
+def saveFace():
89 
+    global last_image_faces
90 
+    for face in last_image_faces:
91 
+        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
92 
+        face = cv2.equalizeHist(face)
93 
+        cv2.imwrite("/home/pi/photos/faces/face-" + str(len(face_db)) + ".jpg", face)
94 
+    loadFaceDB()
95 
+
96 
+def buttonPress():
97 
+    global saved
98 
+    if(GPIO.input(12) == 0):
99 
+        if saved == False:
100 
+            saveFace()
101 
+            saved = True
102 
+            print("Image Saved")
103 
+    else:
104 
+        saved = False
105 
+    if(GPIO.input(16) == 0):
106 
+        if saved == False:
107 
+            saveFace()
108 
+            saved = True
109 
+            print("Image Saved")
110 
+    else:
111 
+        saved = False
112 
+    if(GPIO.input(18) == 0):
113 
+        if saved == False:
114 
+            saveFace()
115 
+            saved = True
116 
+            print("Image Saved")
117 
+    else:
118 
+        saved = False
119 
+
120 
+def loadFaceDB():
121 
+    # Load faces
122 
+    face_db_path='/home/pi/photos/faces'
123 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
124 
+    global face_db
125 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
126 
+    for n in range(0, len(onlyfiles)):
127 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
128 
+
129 
+loadFaceDB()
130 
+show_frame()
131 
+root.mainloop()
132 
+GPIO.cleanup()

+ 129 - 0
face_camera_v2.py
Voir le fichier 

@@ -0,0 +1,129 @@
1 
+import Tkinter as tk
2 
+import cv2, sys, time, os, math
3 
+from PIL import Image, ImageTk
4 
+import numpy as numpy
5 
+
6 
+from os import listdir
7 
+from os.path import isfile, join
8 
+
9 
+import RPi.GPIO as GPIO
10 
+
11 
+# Load the BCM V4l2 driver for /dev/video0
12 
+os.system('sudo modprobe bcm2835-v4l2')
13 
+# Set the framerate ( not sure this does anything! )
14 
+os.system('v4l2-ctl -p 4')
15 
+
16 
+width, height = 320, 240
17 
+cap = cv2.VideoCapture(0)
18 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
19 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
20 
+
21 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
22 
+faceCascade = cv2.CascadeClassifier(cascPath)
23 
+
24 
+root = tk.Tk()
25 
+root.attributes("-fullscreen", True)
26 
+root.bind('<Escape>', lambda e: root.quit())
27 
+
28 
+lmain = tk.Label(root)
29 
+lmain.pack()
30 
+
31 
+last_image_faces = []
32 
+saved = False
33 
+
34 
+GPIO.setmode(GPIO.BOARD)
35 
+GPIO.setup(12, GPIO.IN, pull_up_down = GPIO.PUD_UP)
36 
+GPIO.setup(16, GPIO.IN, pull_up_down = GPIO.PUD_UP)
37 
+GPIO.setup(18, GPIO.IN, pull_up_down = GPIO.PUD_UP)
38 
+
39 
+
40 
+
41 
+def show_frame():
42 
+    _, frame = cap.read()
43 
+    frame = cv2.flip(frame, 1)
44 
+    frame = faceDetect(frame)
45 
+    #frame = cv2.resize(frame, (320,240))
46 
+    buttonPress()
47 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
48 
+    img = Image.fromarray(cv2image)
49 
+    imgtk = ImageTk.PhotoImage(image=img)
50 
+    lmain.imgtk = imgtk
51 
+    lmain.configure(image=imgtk)
52 
+    lmain.after(1, show_frame)
53 
+
54 
+def faceDetect(frame):
55 
+
56 
+    # Do face detection
57 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
58 
+
59 
+    #Slower method
60 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
61 
+    gray = cv2.equalizeHist( gray )
62 
+    faces = faceCascade.detectMultiScale(
63 
+	    gray,
64 
+        scaleFactor=1.1,
65 
+        minNeighbors=4,
66 
+        minSize=(20, 20),
67 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
68 
+    )
69 
+
70 
+    #print "Found {0} faces!".format(len(faces))
71 
+    global last_image_faces
72 
+    image_faces = []
73 
+
74 
+    for (x, y, w, h) in faces:
75 
+        center_x = x + (w/2)
76 
+        center_y = y + (h/2)
77 
+        start_y = center_y - 40
78 
+        start_x = center_x - 40
79 
+        face = gray[y:(y+h), x:(x+w)]
80 
+        image_faces.append(face)
81 
+        # Draw a green rectangle around the face
82 
+        cv2.rectangle(frame, (x, y), (start_x+w, start_y+h), (0, 255, 0), 2)
83 
+    last_image_faces = image_faces
84 
+    return frame
85 
+
86 
+def saveFace():
87 
+    global last_image_faces
88 
+    for face in last_image_faces:
89 
+        face = cv2.resize(face, (120, 120))
90 
+        face = cv2.equalizeHist(face)
91 
+        cv2.imwrite("/home/pi/photos/faces/face-" + str(len(face_db) + 1) + ".jpg", face)
92 
+        print("Saved image " + str(len(face_db)))
93 
+    loadFaceDB()
94 
+
95 
+def buttonPress():
96 
+    global saved
97 
+    if(GPIO.input(12) == 0):
98 
+        if saved == False:
99 
+            saveFace()
100 
+            saved = True
101 
+
102 
+    else:
103 
+        saved = False
104 
+    if(GPIO.input(16) == 0):
105 
+        if saved == False:
106 
+            saveFace()
107 
+            saved = True
108 
+    else:
109 
+        saved = False
110 
+    if(GPIO.input(18) == 0):
111 
+        if saved == False:
112 
+            saveFace()
113 
+            saved = True
114 
+    else:
115 
+        saved = False
116 
+
117 
+def loadFaceDB():
118 
+    # Load faces
119 
+    face_db_path='/home/pi/photos/faces'
120 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
121 
+    global face_db
122 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
123 
+    for n in range(0, len(onlyfiles)):
124 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
125 
+
126 
+loadFaceDB()
127 
+show_frame()
128 
+root.mainloop()
129 
+GPIO.cleanup()

+ 217 - 0
face_recognizer_v1.py
Voir le fichier 

@@ -0,0 +1,217 @@
1 
+#FACE RECOGNIZER
2 
+
3 
+import Tkinter as tk
4 
+import cv2, sys, time, os, math
5 
+from PIL import Image, ImageTk
6 
+import numpy as numpy
7 
+import pprint
8 
+import random
9 
+import math
10 
+
11 
+from os import listdir
12 
+from os.path import isfile, join
13 
+
14 
+# Load the BCM V4l2 driver for /dev/video0
15 
+os.system('sudo modprobe bcm2835-v4l2')
16 
+# Set the framerate ( not sure this does anything! )
17 
+os.system('v4l2-ctl -p 4')
18 
+
19 
+width, height = 320, 240
20 
+cap = cv2.VideoCapture(0)
21 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
22 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
23 
+
24 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
25 
+faceCascade = cv2.CascadeClassifier(cascPath)
26 
+
27 
+root = tk.Tk()
28 
+root.attributes("-fullscreen", True)
29 
+root.bind('<Escape>', lambda e: root.quit())
30 
+
31 
+lmain = tk.Label(root)
32 
+lmain.pack()
33 
+
34 
+last_image_faces = []
35 
+users = []
36 
+
37 
+font = cv2.FONT_HERSHEY_COMPLEX_SMALL
38 
+
39 
+
40 
+
41 
+def show_frame():
42 
+    _, frame = cap.read()
43 
+    frame = cv2.flip(frame, 1)
44 
+    frame = faceDetect(frame)
45 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
46 
+    img = Image.fromarray(cv2image)
47 
+    imgtk = ImageTk.PhotoImage(image=img)
48 
+    lmain.imgtk = imgtk
49 
+    lmain.configure(image=imgtk)
50 
+    lmain.after(1, show_frame)
51 
+
52 
+def faceDetect(frame):
53 
+
54 
+    # Do face detection
55 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
56 
+
57 
+    #Slower method
58 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
59 
+    gray = cv2.equalizeHist( gray )
60 
+    faces = faceCascade.detectMultiScale(
61 
+	    gray,
62 
+        scaleFactor=1.1,
63 
+        minNeighbors=4,
64 
+        minSize=(20, 20),
65 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
66 
+    )
67 
+
68 
+    print "Found {0} faces!".format(len(faces))
69 
+    global last_image_faces
70 
+    image_faces = []
71 
+
72 
+    for (x, y, w, h) in faces:
73 
+        counter = 1
74 
+        center_x = x + (w/2)
75 
+        center_y = y + (h/2)
76 
+        start_y = center_y - 40
77 
+        start_x = center_x - 40
78 
+        if len(last_image_faces) > 0:
79 
+            pos = last_image_faces[0]
80 
+            last_image_faces.remove(pos)
81 
+            dist = math.hypot(center_x - pos[0], center_y - pos[1])
82 
+            if dist < 30:
83 
+
84 
+                # Info = [center_x, center_y, time_since_last_check, user, score]
85 
+                center = [center_x, center_y, pos[2] + 1]
86 
+                print("Tracking face " + str(counter))
87 
+                counter = counter + 1
88 
+                if center[2] > 6:
89 
+                    if start_x > 0 and start_y > 0:
90 
+                        face_crop = frame[y:(y+h), x:(x+w)]
91 
+                        info = recognizeFace(face_crop)
92 
+                        center[2] = 1
93 
+                        center.append(info[0])
94 
+                        center.append(info[1])
95 
+                image_faces.append(center)
96 
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
97 
+                if len(pos) > 3:
98 
+                    center.append(pos[3])
99 
+                    center.append(pos[4])
100 
+                    if pos[4] < 2000:
101 
+                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
102 
+                        cv2.putText(frame, "%.1f" % (center[4]/1000), ((x + w - 38), (y + 17)), font, 1, (0,0,255), 1, 1)
103 
+                    else:
104 
+                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
105 
+                        cv2.putText(frame, users[center[3]], (x, (y + h + 15)), font, 1, (0,255,0), 1, 1)
106 
+                        cv2.putText(frame, "%.1f" % (center[4]/1000), ((x + w - 38), (y + 17)), font, 1, (0,255,0), 1, 1)
107 
+            else:
108 
+                center = [center_x, center_y, 1]
109 
+                image_faces.append(center)
110 
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
111 
+        else:
112 
+            center = [center_x, center_y, 1]
113 
+            image_faces.append(center)
114 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
115 
+    print("Number of faces detected " + str(len(last_image_faces)))
116 
+    last_image_faces = image_faces
117 
+    return frame
118 
+
119 
+def recognizeFace(face):
120 
+    print("Searching Face database...")
121 
+    match_found = False
122 
+    face = cv2.resize(face, (120, 120))
123 
+    face = cv2.cvtColor(face, cv2.cv.CV_BGR2GRAY)
124 
+    face = cv2.equalizeHist( face )
125 
+    cv2.imwrite("/home/pi/photos/faces/face-" + str(len(face_db) + 1) + ".jpg", face)
126 
+    loadFaceDB()
127 
+    predicted_label = predict_image_from_model(model, face)
128 
+    print 'Predicted: %(predicted)s  ' %  {"predicted": users[predicted_label[0]]}
129 
+    print predicted_label[1]
130 
+    return predicted_label
131 
+
132 
+def loadFaceDB():
133 
+    # Load faces
134 
+    face_db_path='/home/pi/photos/faces'
135 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
136 
+    global face_db
137 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
138 
+    for n in range(0, len(onlyfiles)):
139 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
140 
+
141 
+# Face Recognition
142 
+
143 
+def create_and_train_model_from_dict(label_matrix):
144 
+    """ Create eigenface model from dict of labels and images """
145 
+    model = cv2.createEigenFaceRecognizer()
146 
+    model.train(label_matrix.values(), numpy.array(label_matrix.keys()))
147 
+    return model
148 
+
149 
+def predict_image_from_model(model, image):
150 
+    """ Given an eigenface model, predict the label of an image"""
151 
+    return model.predict(image)
152 
+
153 
+def read_csv(filename='/home/pi/faces.csv'):
154 
+    """ Read a csv file """
155 
+    csv = open(filename, 'r')
156 
+    return csv
157 
+
158 
+def prepare_training_testing_data(file):
159 
+    """ prepare testing and training data from file"""
160 
+    lines = file.readlines()
161 
+    training_data, testing_data = split_test_training_data(lines)
162 
+    return training_data
163 
+
164 
+def create_label_matrix_dict(input_file):
165 
+    """ Create dict of label -> matricies from file """
166 
+    ### for every line, if key exists, insert into dict, else append
167 
+    label_dict = {}
168 
+
169 
+    for line in input_file:
170 
+        print(line)
171 
+        ## split on the ';' in the csv separating filename;label
172 
+        filename, label = line.strip().split(';')
173 
+
174 
+        ##update the current key if it exists, else append to it
175 
+        if label_dict.has_key(int(label)):
176 
+            current_files = label_dict.get(label)
177 
+            numpy.append(current_files,read_matrix_from_file(filename))
178 
+        else:
179 
+            label_dict[int(label)] = read_matrix_from_file(filename)
180 
+
181 
+    return label_dict
182 
+
183 
+def split_test_training_data(data, ratio=0.2):
184 
+    """ Split a list of image files by ratio of training:test data """
185 
+    test_size = int(math.floor(ratio*len(data)))
186 
+    random.shuffle(data)
187 
+    return data[test_size:], data[:test_size]
188 
+
189 
+def read_matrix_from_file(filename):
190 
+    """ read in grayscale version of image from file """
191 
+    return cv2.imread(filename, cv2.CV_LOAD_IMAGE_GRAYSCALE)
192 
+
193 
+def create_cvs():
194 
+    BASE_PATH="/home/pi/photos/recognized_faces"
195 
+    SEPARATOR=";"
196 
+    label = 0
197 
+    open("faces.csv", 'w').close()
198 
+    with open("faces.csv", "a") as myfile:
199 
+        for dirname, dirnames, filenames in os.walk(BASE_PATH):
200 
+            for subdirname in dirnames:
201 
+                users.append(subdirname)
202 
+                subject_path = os.path.join(dirname, subdirname)
203 
+                for filename in os.listdir(subject_path):
204 
+                    abs_path = "%s/%s" % (subject_path, filename)
205 
+                    myfile.write("%s%s%d\n" % (abs_path, SEPARATOR, label))
206 
+                label = label + 1
207 
+
208 
+
209 
+# Face Recognition vars
210 
+create_cvs()
211 
+training_data = prepare_training_testing_data(read_csv())
212 
+data_dict = create_label_matrix_dict(training_data)
213 
+model = create_and_train_model_from_dict(data_dict)
214 
+
215 
+loadFaceDB()
216 
+show_frame()
217 
+root.mainloop()

+ 220 - 0
face_recognizer_v2.py
Voir le fichier 

@@ -0,0 +1,220 @@
1 
+#FACE RECOGNIZER
2 
+
3 
+import Tkinter as tk
4 
+import cv2, sys, time, os, math
5 
+from PIL import Image, ImageTk
6 
+import numpy as numpy
7 
+import pprint
8 
+import random
9 
+import math
10 
+from os import listdir
11 
+from os.path import isfile, join
12 
+import zerorpc
13 
+
14 
+# Load the BCM V4l2 driver for /dev/video0
15 
+os.system('sudo modprobe bcm2835-v4l2')
16 
+# Set the framerate ( not sure this does anything! )
17 
+os.system('v4l2-ctl -p 4')
18 
+
19 
+width, height = 320, 240
20 
+cap = cv2.VideoCapture(0)
21 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
22 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
23 
+
24 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
25 
+faceCascade = cv2.CascadeClassifier(cascPath)
26 
+
27 
+root = tk.Tk()
28 
+root.attributes("-fullscreen", True)
29 
+root.bind('<Escape>', lambda e: root.quit())
30 
+
31 
+lmain = tk.Label(root)
32 
+lmain.pack()
33 
+
34 
+last_image_faces = []
35 
+users = []
36 
+
37 
+font = cv2.FONT_HERSHEY_COMPLEX_SMALL
38 
+
39 
+c = zerorpc.Client()
40 
+c.connect("tcp://192.168.1.40:4242")
41 
+
42 
+def show_frame():
43 
+    _, frame = cap.read()
44 
+    frame = cv2.flip(frame, 1)
45 
+    frame = faceDetect(frame)
46 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
47 
+    img = Image.fromarray(cv2image)
48 
+    imgtk = ImageTk.PhotoImage(image=img)
49 
+    lmain.imgtk = imgtk
50 
+    lmain.configure(image=imgtk)
51 
+    lmain.after(1, show_frame)
52 
+
53 
+def faceDetect(frame):
54 
+
55 
+    # Do face detection
56 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
57 
+
58 
+    #Slower method
59 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
60 
+    gray = cv2.equalizeHist( gray )
61 
+    faces = faceCascade.detectMultiScale(
62 
+	    gray,
63 
+        scaleFactor=1.1,
64 
+        minNeighbors=4,
65 
+        minSize=(20, 20),
66 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
67 
+    )
68 
+
69 
+    print "Found {0} faces!".format(len(faces))
70 
+    global last_image_faces
71 
+    image_faces = []
72 
+
73 
+    for (x, y, w, h) in faces:
74 
+        counter = 1
75 
+        center_x = x + (w/2)
76 
+        center_y = y + (h/2)
77 
+        start_y = center_y - 40
78 
+        start_x = center_x - 40
79 
+        if len(last_image_faces) > 0:
80 
+            pos = last_image_faces[0]
81 
+            last_image_faces.remove(pos)
82 
+            dist = math.hypot(center_x - pos[0], center_y - pos[1])
83 
+            if dist < 30:
84 
+
85 
+                # Info = [center_x, center_y, time_since_last_check, user, score]
86 
+                center = [center_x, center_y, pos[2] + 1]
87 
+                print("Tracking face " + str(counter))
88 
+                counter = counter + 1
89 
+                if center[2] > 6:
90 
+                    if start_x > 0 and start_y > 0:
91 
+                        face_crop = frame[y:(y+h), x:(x+w)]
92 
+                        info = recognizeFace(face_crop)
93 
+                        center[2] = 1
94 
+                        center.append(info[0])
95 
+                        center.append(info[1])
96 
+                image_faces.append(center)
97 
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
98 
+                if len(pos) > 3:
99 
+                    center.append(pos[3])
100 
+                    center.append(pos[4])
101 
+                    if pos[4] < 2000:
102 
+                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
103 
+                        cv2.putText(frame, "%.1f" % (center[4]/1000), ((x + w - 38), (y + 17)), font, 1, (0,0,255), 1, 1)
104 
+                    else:
105 
+                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
106 
+                        cv2.putText(frame, users[center[3]], (x, (y + h + 15)), font, 1, (0,255,0), 1, 1)
107 
+                        cv2.putText(frame, "%.1f" % (center[4]/1000), ((x + w - 38), (y + 17)), font, 1, (0,255,0), 1, 1)
108 
+            else:
109 
+                center = [center_x, center_y, 1]
110 
+                image_faces.append(center)
111 
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
112 
+        else:
113 
+            center = [center_x, center_y, 1]
114 
+            image_faces.append(center)
115 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
116 
+    print("Number of faces detected " + str(len(last_image_faces)))
117 
+    last_image_faces = image_faces
118 
+    return frame
119 
+
120 
+def recognizeFace(face):
121 
+    print("Searching Face database...")
122 
+    match_found = False
123 
+    face = cv2.resize(face, (120, 120))
124 
+    face = cv2.cvtColor(face, cv2.cv.CV_BGR2GRAY)
125 
+    face = cv2.equalizeHist( face )
126 
+    cv2.imwrite("/home/pi/photos/faces/face-" + str(len(face_db) + 1) + ".jpg", face)
127 
+    loadFaceDB()
128 
+    predicted_label = predict_image_from_model(model, face)
129 
+    print 'Predicted: %(predicted)s  ' %  {"predicted": users[predicted_label[0]]}
130 
+    print str(predicted_label[0]) + " - " + str(predicted_label[1])
131 
+    print c.face_recognized(users[predicted_label[0]])
132 
+    return predicted_label
133 
+
134 
+def loadFaceDB():
135 
+    # Load faces
136 
+    face_db_path='/home/pi/photos/faces'
137 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
138 
+    global face_db
139 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
140 
+    for n in range(0, len(onlyfiles)):
141 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
142 
+
143 
+# Face Recognition
144 
+
145 
+def create_and_train_model_from_dict(label_matrix):
146 
+    """ Create eigenface model from dict of labels and images """
147 
+    model = cv2.createEigenFaceRecognizer()
148 
+    model.train(label_matrix.values(), numpy.array(label_matrix.keys()))
149 
+    return model
150 
+
151 
+def predict_image_from_model(model, image):
152 
+    """ Given an eigenface model, predict the label of an image"""
153 
+    return model.predict(image)
154 
+
155 
+def read_csv(filename='/home/pi/faces.csv'):
156 
+    """ Read a csv file """
157 
+    csv = open(filename, 'r')
158 
+    return csv
159 
+
160 
+def prepare_training_testing_data(file):
161 
+    """ prepare testing and training data from file"""
162 
+    lines = file.readlines()
163 
+    training_data, testing_data = split_test_training_data(lines)
164 
+    return training_data
165 
+
166 
+def create_label_matrix_dict(input_file):
167 
+    """ Create dict of label -> matricies from file """
168 
+    ### for every line, if key exists, insert into dict, else append
169 
+    label_dict = {}
170 
+
171 
+    for line in input_file:
172 
+        print(line)
173 
+        ## split on the ';' in the csv separating filename;label
174 
+        filename, label = line.strip().split(';')
175 
+
176 
+        ##update the current key if it exists, else append to it
177 
+        if label_dict.has_key(int(label)):
178 
+            current_files = label_dict.get(label)
179 
+            numpy.append(current_files,read_matrix_from_file(filename))
180 
+        else:
181 
+            label_dict[int(label)] = read_matrix_from_file(filename)
182 
+
183 
+    return label_dict
184 
+
185 
+def split_test_training_data(data, ratio=0.2):
186 
+    """ Split a list of image files by ratio of training:test data """
187 
+    test_size = int(math.floor(ratio*len(data)))
188 
+    random.shuffle(data)
189 
+    return data[test_size:], data[:test_size]
190 
+
191 
+def read_matrix_from_file(filename):
192 
+    """ read in grayscale version of image from file """
193 
+    return cv2.imread(filename, cv2.CV_LOAD_IMAGE_GRAYSCALE)
194 
+
195 
+def create_csv():
196 
+    BASE_PATH="/home/pi/photos/recognized_faces"
197 
+    SEPARATOR=";"
198 
+    label = 0
199 
+    open("/home/pi/faces.csv", 'w').close()
200 
+    with open("/home/pi/faces.csv", "a") as myfile:
201 
+        for dirname, dirnames, filenames in os.walk(BASE_PATH):
202 
+            for subdirname in dirnames:
203 
+                users.append(subdirname)
204 
+                subject_path = os.path.join(dirname, subdirname)
205 
+                for filename in os.listdir(subject_path):
206 
+                    abs_path = "%s/%s" % (subject_path, filename)
207 
+                    myfile.write("%s%s%d\n" % (abs_path, SEPARATOR, label))
208 
+                label = label + 1
209 
+
210 
+
211 
+# Face Recognition vars
212 
+create_csv()
213 
+training_data = prepare_training_testing_data(read_csv())
214 
+data_dict = create_label_matrix_dict(training_data)
215 
+model = create_and_train_model_from_dict(data_dict)
216 
+
217 
+loadFaceDB()
218 
+show_frame()
219 
+
220 
+root.mainloop()

+ 224 - 0
face_recognizer_v3.py
Voir le fichier 

@@ -0,0 +1,224 @@
1 
+#FACE RECOGNIZER
2 
+
3 
+#import Tkinter as tk
4 
+import cv2, sys, time, os, math
5 
+from PIL import Image, ImageTk
6 
+import numpy as numpy
7 
+import pprint
8 
+import random
9 
+import math
10 
+from os import listdir
11 
+from os.path import isfile, join
12 
+import zerorpc
13 
+
14 
+# Load the BCM V4l2 driver for /dev/video0
15 
+os.system('sudo modprobe bcm2835-v4l2')
16 
+# Set the framerate ( not sure this does anything! )
17 
+#os.system('v4l2-ctl -p 4')
18 
+
19 
+width, height = 320, 240
20 
+cap = cv2.VideoCapture()
21 
+cap.open(0)
22 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  width)
23 
+cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
24 
+
25 
+cascPath = '/Users/james/dev/betabot-python-tests/lbpcascade_frontalface.xml'
26 
+faceCascade = cv2.CascadeClassifier(cascPath)
27 
+
28 
+#root = tk.Tk()
29 
+#root.attributes("-fullscreen", True)
30 
+#root.bind('<Escape>', lambda e: root.quit())
31 
+
32 
+#lmain = tk.Label(root)
33 
+#lmain.pack()
34 
+
35 
+last_image_faces = []
36 
+users = []
37 
+
38 
+font = cv2.FONT_HERSHEY_COMPLEX_SMALL
39 
+
40 
+c = zerorpc.Client()
41 
+c.connect("tcp://192.168.1.40:4242")
42 
+
43 
+def show_frame():
44 
+    _, frame = cap.read()
45 
+    frame = cv2.flip(frame, 1)
46 
+    frame = faceDetect(frame)
47 
+    cv2.imshow("webcam",frame);
48 
+    cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
49 
+    img = Image.fromarray(cv2image)
50 
+    #imgtk = ImageTk.PhotoImage(image=img)
51 
+    #lmain.imgtk = imgtk
52 
+    #lmain.configure(image=imgtk)
53 
+    #lmain.after(1, show_frame)
54 
+
55 
+def faceDetect(frame):
56 
+
57 
+    # Do face detection
58 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
59 
+
60 
+    #Slower method
61 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
62 
+    gray = cv2.equalizeHist( gray )
63 
+    faces = faceCascade.detectMultiScale(
64 
+	    gray,
65 
+        scaleFactor=1.1,
66 
+        minNeighbors=4,
67 
+        minSize=(20, 20),
68 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
69 
+    )
70 
+
71 
+    print "Found {0} faces!".format(len(faces))
72 
+    global last_image_faces
73 
+    image_faces = []
74 
+
75 
+    for (x, y, w, h) in faces:
76 
+        counter = 1
77 
+        center_x = x + (w/2)
78 
+        center_y = y + (h/2)
79 
+        start_y = center_y - 40
80 
+        start_x = center_x - 40
81 
+        if len(last_image_faces) > 0:
82 
+            pos = last_image_faces[0]
83 
+            last_image_faces.remove(pos)
84 
+            dist = math.hypot(center_x - pos[0], center_y - pos[1])
85 
+            if dist < 30:
86 
+
87 
+                # Info = [center_x, center_y, time_since_last_check, user, score]
88 
+                center = [center_x, center_y, pos[2] + 1]
89 
+                print("Tracking face " + str(counter))
90 
+                counter = counter + 1
91 
+                if center[2] > 6:
92 
+                    if start_x > 0 and start_y > 0:
93 
+                        face_crop = frame[y:(y+h), x:(x+w)]
94 
+                        info = recognizeFace(face_crop)
95 
+                        center[2] = 1
96 
+                        center.append(info[0])
97 
+                        center.append(info[1])
98 
+                image_faces.append(center)
99 
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
100 
+                if len(pos) > 3:
101 
+                    center.append(pos[3])
102 
+                    center.append(pos[4])
103 
+                    if pos[4] < 2000:
104 
+                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
105 
+                        cv2.putText(frame, "%.1f" % (center[4]/1000), ((x + w - 38), (y + 17)), font, 1, (0,0,255), 1, 1)
106 
+                    else:
107 
+                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
108 
+                        cv2.putText(frame, users[center[3]], (x, (y + h + 15)), font, 1, (0,255,0), 1, 1)
109 
+                        cv2.putText(frame, "%.1f" % (center[4]/1000), ((x + w - 38), (y + 17)), font, 1, (0,255,0), 1, 1)
110 
+            else:
111 
+                center = [center_x, center_y, 1]
112 
+                image_faces.append(center)
113 
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
114 
+        else:
115 
+            center = [center_x, center_y, 1]
116 
+            image_faces.append(center)
117 
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
118 
+    print("Number of faces detected " + str(len(last_image_faces)))
119 
+    last_image_faces = image_faces
120 
+    return frame
121 
+
122 
+def recognizeFace(face):
123 
+    print("Searching Face database...")
124 
+    match_found = False
125 
+    face = cv2.resize(face, (120, 120))
126 
+    face = cv2.cvtColor(face, cv2.cv.CV_BGR2GRAY)
127 
+    face = cv2.equalizeHist( face )
128 
+    cv2.imwrite("/Users/james/dev/betabot-python-tests/photos/faces/face-" + str(len(face_db) + 1) + ".jpg", face)
129 
+    loadFaceDB()
130 
+    predicted_label = predict_image_from_model(model, face)
131 
+    print 'Predicted: %(predicted)s  ' %  {"predicted": users[predicted_label[0]]}
132 
+    print str(predicted_label[0]) + " - " + str(predicted_label[1])
133 
+    print c.face_recognized(users[predicted_label[0]])
134 
+    return predicted_label
135 
+
136 
+def loadFaceDB():
137 
+    # Load faces
138 
+    face_db_path='/Users/james/dev/betabot-python-tests/photos/faces'
139 
+    onlyfiles = [ f for f in listdir(face_db_path) if isfile(join(face_db_path,f)) ]
140 
+    global face_db
141 
+    face_db = numpy.empty(len(onlyfiles), dtype=object)
142 
+    for n in range(0, len(onlyfiles)):
143 
+      face_db[n] = cv2.imread( join(face_db_path,onlyfiles[n]) )
144 
+
145 
+# Face Recognition
146 
+
147 
+def create_and_train_model_from_dict(label_matrix):
148 
+    """ Create eigenface model from dict of labels and images """
149 
+    model = cv2.createEigenFaceRecognizer()
150 
+    model.train(label_matrix.values(), numpy.array(label_matrix.keys()))
151 
+    return model
152 
+
153 
+def predict_image_from_model(model, image):
154 
+    """ Given an eigenface model, predict the label of an image"""
155 
+    return model.predict(image)
156 
+
157 
+def read_csv(filename='/Users/james/dev/betabot-python-tests/faces.csv'):
158 
+    """ Read a csv file """
159 
+    csv = open(filename, 'r')
160 
+    return csv
161 
+
162 
+def prepare_training_testing_data(file):
163 
+    """ prepare testing and training data from file"""
164 
+    lines = file.readlines()
165 
+    training_data, testing_data = split_test_training_data(lines)
166 
+    return training_data
167 
+
168 
+def create_label_matrix_dict(input_file):
169 
+    """ Create dict of label -> matricies from file """
170 
+    ### for every line, if key exists, insert into dict, else append
171 
+    label_dict = {}
172 
+
173 
+    for line in input_file:
174 
+        print(line)
175 
+        ## split on the ';' in the csv separating filename;label
176 
+        filename, label = line.strip().split(';')
177 
+
178 
+        ##update the current key if it exists, else append to it
179 
+        if label_dict.has_key(int(label)):
180 
+            current_files = label_dict.get(label)
181 
+            numpy.append(current_files,read_matrix_from_file(filename))
182 
+        else:
183 
+            label_dict[int(label)] = read_matrix_from_file(filename)
184 
+
185 
+    return label_dict
186 
+
187 
+def split_test_training_data(data, ratio=0.2):
188 
+    """ Split a list of image files by ratio of training:test data """
189 
+    test_size = int(math.floor(ratio*len(data)))
190 
+    random.shuffle(data)
191 
+    return data[test_size:], data[:test_size]
192 
+
193 
+def read_matrix_from_file(filename):
194 
+    """ read in grayscale version of image from file """
195 
+    return cv2.imread(filename, cv2.CV_LOAD_IMAGE_GRAYSCALE)
196 
+
197 
+def create_csv():
198 
+    BASE_PATH="/Users/james/dev/betabot-python-tests/photos/recognized_faces"
199 
+    SEPARATOR=";"
200 
+    label = 0
201 
+    open("/Users/james/dev/betabot-python-tests/faces.csv", 'w').close()
202 
+    with open("/Users/james/dev/betabot-python-tests/faces.csv", "a") as myfile:
203 
+        for dirname, dirnames, filenames in os.walk(BASE_PATH):
204 
+            for subdirname in dirnames:
205 
+                users.append(subdirname)
206 
+                subject_path = os.path.join(dirname, subdirname)
207 
+                for filename in os.listdir(subject_path):
208 
+                    abs_path = "%s/%s" % (subject_path, filename)
209 
+                    myfile.write("%s%s%d\n" % (abs_path, SEPARATOR, label))
210 
+                label = label + 1
211 
+
212 
+
213 
+# Face Recognition vars
214 
+create_csv()
215 
+training_data = prepare_training_testing_data(read_csv())
216 
+data_dict = create_label_matrix_dict(training_data)
217 
+model = create_and_train_model_from_dict(data_dict)
218 
+
219 
+loadFaceDB()
220 
+
221 
+while True:
222 
+    show_frame()
223 
+
224 
+#root.mainloop()

+ 122 - 0
facetracker.py
Voir le fichier 

@@ -0,0 +1,122 @@
1 
+#!/usr/bin/env python
2 
+
3 
+import cv2, sys, time, os
4 
+#from pantilt import *
5 
+import Tkinter as tk
6 
+import PIL
7 
+from PIL import ImageTk
8 
+from PIL import Image
9 
+
10 
+# Load the BCM V4l2 driver for /dev/video0
11 
+os.system('sudo modprobe bcm2835-v4l2')
12 
+# Set the framerate ( not sure this does anything! )
13 
+os.system('v4l2-ctl -p 4')
14 
+
15 
+# Frame Size. Smaller is faster, but less accurate.
16 
+# Wide and short is better, since moving your head
17 
+# vertically is kinda hard!
18 
+FRAME_W = 320
19 
+FRAME_H = 240
20 
+
21 
+# Default Pan/Tilt for the camera in degrees.
22 
+# Camera range is from 0 to 180
23 
+cam_pan = 70
24 
+cam_tilt = 70
25 
+
26 
+# Set up the CascadeClassifier for face tracking
27 
+#cascPath = 'haarcascade_frontalface_default.xml' # sys.argv[1]
28 
+cascPath = '/home/pi/lbpcascade_frontalface.xml'
29 
+faceCascade = cv2.CascadeClassifier(cascPath)
30 
+
31 
+# Set up the capture with our frame size
32 
+video_capture = cv2.VideoCapture(0)
33 
+video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,  FRAME_W)
34 
+video_capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, FRAME_H)
35 
+time.sleep(2)
36 
+
37 
+window = tk.Tk()  #Makes main window
38 
+window.wm_title("Digital Microscope")
39 
+window.config(background="#FFFFFF")
40 
+#window.attributes("-fullscreen", True)
41 
+
42 
+# Turn the camera to the default position
43 
+#pan(cam_pan)
44 
+#tilt(cam_tilt)
45 
+
46 
+window.mainloop()
47 
+
48 
+while True:
49 
+    # Capture frame-by-frame
50 
+    ret, frame = video_capture.read()
51 
+
52 
+    if ret == False:
53 
+      print("Error getting image")
54 
+      continue
55 
+
56 
+    # Convert to greyscale for detection
57 
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
58 
+    gray = cv2.equalizeHist( gray )
59 
+
60 
+    # Do face detection
61 
+    #faces = faceCascade.detectMultiScale(frame, 1.1, 3, 0, (10, 10))
62 
+
63 
+    # Slower method
64 
+    faces = faceCascade.detectMultiScale(
65 
+	gray,
66 
+        scaleFactor=1.1,
67 
+        minNeighbors=4,
68 
+        minSize=(20, 20),
69 
+        flags=cv2.cv.CV_HAAR_SCALE_IMAGE | cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
70 
+    )
71 
+
72 
+    print "Found {0} faces!".format(len(faces))
73 
+
74 
+    for (x, y, w, h) in faces:
75 
+        # Draw a green rectangle around the face
76 
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
77 
+
78 
+        # Track first face
79 
+
80 
+        # Get the center of the face
81 
+
82 
+        # Correct relative to center of image
83 
+        turn_x  = float(x - (FRAME_W/2))
84 
+        turn_y  = float(y - (FRAME_H/2))
85 
+
86 
+        # Convert to percentage offset
87 
+        turn_x  /= float(FRAME_W/2)
88 
+        turn_y  /= float(FRAME_H/2)
89 
+
90 
+        # Scale offset to degrees
91 
+        turn_x   *= 2.5 # VFOV
92 
+        turn_y   *= 2.5 # HFOV
93 
+        cam_pan  += -turn_x
94 
+        cam_tilt += turn_y
95 
+
96 
+        # Clamp Pan/Tilt to 0 to 180 degrees
97 
+        # cam_pan = max(0,min(180,cam_pan))
98 
+        # cam_tilt = max(0,min(180,cam_tilt))
99 
+
100 
+        # Update the servos
101 
+        # pan(cam_pan)
102 
+        # tilt(cam_tilt)
103 
+
104 
+        break
105 
+
106 
+    # Display the image, with rectangle
107 
+    # on the Pi desktop
108 
+    #cv2.imshow('Video', frame)
109 
+    b,g,r = cv2.split(frame)
110 
+    img = cv2.merge((r,g,b))
111 
+    im = Image.fromarray(img)
112 
+    img = ImageTk.PhotoImage(image=im)
113 
+    #img = ImageTk.PhotoImage(frame)
114 
+    panel = tk.Label(window, image = img)
115 
+
116 
+
117 
+    if cv2.waitKey(1) & 0xFF == ord('q'):
118 
+        break
119 
+
120 
+# When everything is done, release the capture
121 
+video_capture.release()
122 
+cv2.destroyAllWindows()

+ 1505 - 0
lbpcascade_frontalface.xml
Voir le fichier 

@@ -0,0 +1,1505 @@
1 
+<?xml version="1.0"?>
2 
+<!--
3 
+number of positive samples 3000
4 
+number of negative samples 1500
5 
+-->
6 
+<opencv_storage>
7 
+<cascade type_id="opencv-cascade-classifier">
8 
+  <stageType>BOOST</stageType>
9 
+  <featureType>LBP</featureType>
10 
+  <height>24</height>
11 
+  <width>24</width>
12 
+  <stageParams>
13 
+    <boostType>GAB</boostType>
14 
+    <minHitRate>0.9950000047683716</minHitRate>
15 
+    <maxFalseAlarm>0.5000000000000000</maxFalseAlarm>
16 
+    <weightTrimRate>0.9500000000000000</weightTrimRate>
17 
+    <maxDepth>1</maxDepth>
18 
+    <maxWeakCount>100</maxWeakCount></stageParams>
19 
+  <featureParams>
20 
+    <maxCatCount>256</maxCatCount></featureParams>
21 
+  <stageNum>20</stageNum>
22 
+  <stages>
23 
+    <!-- stage 0 -->
24 
+    <_>
25 
+      <maxWeakCount>3</maxWeakCount>
26 
+      <stageThreshold>-0.7520892024040222</stageThreshold>
27 
+      <weakClassifiers>
28 
+        <!-- tree 0 -->
29 
+        <_>
30 
+          <internalNodes>
31 
+            0 -1 46 -67130709 -21569 -1426120013 -1275125205 -21585
32 
+            -16385 587145899 -24005</internalNodes>
33 
+          <leafValues>
34 
+            -0.6543210148811340 0.8888888955116272</leafValues></_>
35 
+        <!-- tree 1 -->
36 
+        <_>
37 
+          <internalNodes>
38 
+            0 -1 13 -163512766 -769593758 -10027009 -262145 -514457854
39 
+            -193593353 -524289 -1</internalNodes>
40 
+          <leafValues>
41 
+            -0.7739216089248657 0.7278633713722229</leafValues></_>
42 
+        <!-- tree 2 -->
43 
+        <_>
44 
+          <internalNodes>
45 
+            0 -1 2 -363936790 -893203669 -1337948010 -136907894
46 
+            1088782736 -134217726 -741544961 -1590337</internalNodes>
47 
+          <leafValues>
48 
+            -0.7068563103675842 0.6761534214019775</leafValues></_></weakClassifiers></_>
49 
+    <!-- stage 1 -->
50 
+    <_>
51 
+      <maxWeakCount>4</maxWeakCount>
52 
+      <stageThreshold>-0.4872078299522400</stageThreshold>
53 
+      <weakClassifiers>
54 
+        <!-- tree 0 -->
55 
+        <_>
56 
+          <internalNodes>
57 
+            0 -1 84 2147483647 1946124287 -536870913 2147450879
58 
+            738132490 1061101567 243204619 2147446655</internalNodes>
59 
+          <leafValues>
60 
+            -0.8083735704421997 0.7685696482658386</leafValues></_>
61 
+        <!-- tree 1 -->
62 
+        <_>
63 
+          <internalNodes>
64 
+            0 -1 21 2147483647 263176079 1879048191 254749487 1879048191
65 
+            -134252545 -268435457 801111999</internalNodes>
66 
+          <leafValues>
67 
+            -0.7698410153388977 0.6592915654182434</leafValues></_>
68 
+        <!-- tree 2 -->
69 
+        <_>
70 
+          <internalNodes>
71 
+            0 -1 106 -98110272 1610939566 -285484400 -850010381
72 
+            -189334372 -1671954433 -571026695 -262145</internalNodes>
73 
+          <leafValues>
74 
+            -0.7506558895111084 0.5444605946540833</leafValues></_>
75 
+        <!-- tree 3 -->
76 
+        <_>
77 
+          <internalNodes>
78 
+            0 -1 48 -798690576 -131075 1095771153 -237144073 -65569 -1
79 
+            -216727745 -69206049</internalNodes>
80 
+          <leafValues>
81 
+            -0.7775990366935730 0.5465461611747742</leafValues></_></weakClassifiers></_>
82 
+    <!-- stage 2 -->
83 
+    <_>
84 
+      <maxWeakCount>4</maxWeakCount>
85 
+      <stageThreshold>-1.1592328548431396</stageThreshold>
86 
+      <weakClassifiers>
87 
+        <!-- tree 0 -->
88 
+        <_>
89 
+          <internalNodes>
90 
+            0 -1 47 -21585 -20549 -100818262 -738254174 -20561 -36865
91 
+            -151016790 -134238549</internalNodes>
92 
+          <leafValues>
93 
+            -0.5601882934570313 0.7743113040924072</leafValues></_>
94 
+        <!-- tree 1 -->
95 
+        <_>
96 
+          <internalNodes>
97 
+            0 -1 12 -286003217 183435247 -268994614 -421330945
98 
+            -402686081 1090387966 -286785545 -402653185</internalNodes>
99 
+          <leafValues>
100 
+            -0.6124526262283325 0.6978127956390381</leafValues></_>
101 
+        <!-- tree 2 -->
102 
+        <_>
103 
+          <internalNodes>
104 
+            0 -1 26 -50347012 970882927 -50463492 -1253377 -134218251
105 
+            -50364513 -33619992 -172490753</internalNodes>
106 
+          <leafValues>
107 
+            -0.6114496588706970 0.6537628173828125</leafValues></_>
108 
+        <!-- tree 3 -->
109 
+        <_>
110 
+          <internalNodes>
111 
+            0 -1 8 -273 -135266321 1877977738 -2088243418 -134217987
112 
+            2146926575 -18910642 1095231247</internalNodes>
113 
+          <leafValues>
114 
+            -0.6854077577590942 0.5403239130973816</leafValues></_></weakClassifiers></_>
115 
+    <!-- stage 3 -->
116 
+    <_>
117 
+      <maxWeakCount>5</maxWeakCount>
118 
+      <stageThreshold>-0.7562355995178223</stageThreshold>
119 
+      <weakClassifiers>
120 
+        <!-- tree 0 -->
121 
+        <_>
122 
+          <internalNodes>
123 
+            0 -1 96 -1273 1870659519 -20971602 -67633153 -134250731
124 
+            2004875127 -250 -150995969</internalNodes>
125 
+          <leafValues>
126 
+            -0.4051094949245453 0.7584033608436585</leafValues></_>
127 
+        <!-- tree 1 -->
128 
+        <_>
129 
+          <internalNodes>
130 
+            0 -1 33 -868162224 -76810262 -4262145 -257 1465211989
131 
+            -268959873 -2656269 -524289</internalNodes>
132 
+          <leafValues>
133 
+            -0.7388162612915039 0.5340843200683594</leafValues></_>
134 
+        <!-- tree 2 -->
135 
+        <_>
136 
+          <internalNodes>
137 
+            0 -1 57 -12817 -49 -541103378 -152950 -38993 -20481 -1153876
138 
+            -72478976</internalNodes>
139 
+          <leafValues>
140 
+            -0.6582943797111511 0.5339496731758118</leafValues></_>
141 
+        <!-- tree 3 -->
142 
+        <_>
143 
+          <internalNodes>
144 
+            0 -1 125 -269484161 -452984961 -319816180 -1594032130 -2111
145 
+            -990117891 -488975296 -520947741</internalNodes>
146 
+          <leafValues>
147 
+            -0.5981323719024658 0.5323504805564880</leafValues></_>
148 
+        <!-- tree 4 -->
149 
+        <_>
150 
+          <internalNodes>
151 
+            0 -1 53 557787431 670265215 -1342193665 -1075892225
152 
+            1998528318 1056964607 -33570977 -1</internalNodes>
153 
+          <leafValues>
154 
+            -0.6498787999153137 0.4913350641727448</leafValues></_></weakClassifiers></_>
155 
+    <!-- stage 4 -->
156 
+    <_>
157 
+      <maxWeakCount>5</maxWeakCount>
158 
+      <stageThreshold>-0.8085358142852783</stageThreshold>
159 
+      <weakClassifiers>
160 
+        <!-- tree 0 -->
161 
+        <_>
162 
+          <internalNodes>
163 
+            0 -1 60 -536873708 880195381 -16842788 -20971521 -176687276
164 
+            -168427659 -16777260 -33554626</internalNodes>
165 
+          <leafValues>
166 
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