Merge branch 'release/1.1'

track multiple faces/demo - track multiple faces.py

@@ -0,0 +1,276 @@
+#!/usr/bin/python
+'''
+    Author: Guido Diepen <[email protected]>
+'''
+
+#Import the OpenCV and dlib libraries
+import cv2
+import dlib
+
+
+import threading
+import time
+
+#Initialize a face cascade using the frontal face haar cascade provided with
+#the OpenCV library
+#Make sure that you copy this file from the opencv project to the root of this
+#project folder
+faceCascade = cv2.CascadeClassifier('../haarcascade_frontalface_default.xml')
+
+#The deisred output width and height
+OUTPUT_SIZE_WIDTH = 775
+OUTPUT_SIZE_HEIGHT = 600
+
+
+#We are not doing really face recognition
+def doRecognizePerson(faceNames, fid):
+    time.sleep(2)
+    faceNames[ fid ] = "Person " + str(fid)
+
+
+
+
+def detectAndTrackMultipleFaces():
+    #Open the first webcame device
+    capture = cv2.VideoCapture(0)
+
+    #Create two opencv named windows
+    cv2.namedWindow("base-image", cv2.WINDOW_AUTOSIZE)
+    cv2.namedWindow("result-image", cv2.WINDOW_AUTOSIZE)
+
+    #Position the windows next to eachother
+    cv2.moveWindow("base-image",0,100)
+    cv2.moveWindow("result-image",400,100)
+
+    #Start the window thread for the two windows we are using
+    cv2.startWindowThread()
+
+    #The color of the rectangle we draw around the face
+    rectangleColor = (0,165,255)
+
+    #variables holding the current frame number and the current faceid
+    frameCounter = 0
+    currentFaceID = 0
+
+    #Variables holding the correlation trackers and the name per faceid
+    faceTrackers = {}
+    faceNames = {}
+
+    try:
+        while True:
+            #Retrieve the latest image from the webcam
+            rc,fullSizeBaseImage = capture.read()
+
+            #Resize the image to 320x240
+            baseImage = cv2.resize( fullSizeBaseImage, ( 320, 240))
+
+            #Check if a key was pressed and if it was Q, then break
+            #from the infinite loop
+            pressedKey = cv2.waitKey(2)
+            if pressedKey == ord('Q'):
+                break
+
+
+
+            #Result image is the image we will show the user, which is a
+            #combination of the original image from the webcam and the
+            #overlayed rectangle for the largest face
+            resultImage = baseImage.copy()
+
+
+
+
+            #STEPS:
+            # * Update all trackers and remove the ones that are not 
+            #   relevant anymore
+            # * Every 10 frames:
+            #       + Use face detection on the current frame and look
+            #         for faces. 
+            #       + For each found face, check if centerpoint is within
+            #         existing tracked box. If so, nothing to do
+            #       + If centerpoint is NOT in existing tracked box, then
+            #         we add a new tracker with a new face-id
+
+
+            #Increase the framecounter
+            frameCounter += 1 
+
+
+
+            #Update all the trackers and remove the ones for which the update
+            #indicated the quality was not good enough
+            fidsToDelete = []
+            for fid in faceTrackers.keys():
+                trackingQuality = faceTrackers[ fid ].update( baseImage )
+
+                #If the tracking quality is good enough, we must delete
+                #this tracker
+                if trackingQuality < 7:
+                    fidsToDelete.append( fid )
+
+            for fid in fidsToDelete:
+                print("Removing fid " + str(fid) + " from list of trackers")
+                faceTrackers.pop( fid , None )
+
+
+
+
+            #Every 10 frames, we will have to determine which faces
+            #are present in the frame
+            if (frameCounter % 10) == 0:
+
+
+
+                #For the face detection, we need to make use of a gray
+                #colored image so we will convert the baseImage to a
+                #gray-based image
+                gray = cv2.cvtColor(baseImage, cv2.COLOR_BGR2GRAY)
+                #Now use the haar cascade detector to find all faces
+                #in the image
+                faces = faceCascade.detectMultiScale(gray, 1.3, 5)
+
+
+
+                #Loop over all faces and check if the area for this
+                #face is the largest so far
+                #We need to convert it to int here because of the
+                #requirement of the dlib tracker. If we omit the cast to
+                #int here, you will get cast errors since the detector
+                #returns numpy.int32 and the tracker requires an int
+                for (_x,_y,_w,_h) in faces:
+                    x = int(_x)
+                    y = int(_y)
+                    w = int(_w)
+                    h = int(_h)
+
+
+                    #calculate the centerpoint
+                    x_bar = x + 0.5 * w
+                    y_bar = y + 0.5 * h
+
+
+
+                    #Variable holding information which faceid we 
+                    #matched with
+                    matchedFid = None
+
+                    #Now loop over all the trackers and check if the 
+                    #centerpoint of the face is within the box of a 
+                    #tracker
+                    for fid in faceTrackers.keys():
+                        tracked_position =  faceTrackers[fid].get_position()
+
+                        t_x = int(tracked_position.left())
+                        t_y = int(tracked_position.top())
+                        t_w = int(tracked_position.width())
+                        t_h = int(tracked_position.height())
+
+
+                        #calculate the centerpoint
+                        t_x_bar = t_x + 0.5 * t_w
+                        t_y_bar = t_y + 0.5 * t_h
+
+                        #check if the centerpoint of the face is within the 
+                        #rectangleof a tracker region. Also, the centerpoint
+                        #of the tracker region must be within the region 
+                        #detected as a face. If both of these conditions hold
+                        #we have a match
+                        if ( ( t_x <= x_bar   <= (t_x + t_w)) and 
+                             ( t_y <= y_bar   <= (t_y + t_h)) and 
+                             ( x   <= t_x_bar <= (x   + w  )) and 
+                             ( y   <= t_y_bar <= (y   + h  ))):
+                            matchedFid = fid
+
+
+                    #If no matched fid, then we have to create a new tracker
+                    if matchedFid is None:
+
+                        print("Creating new tracker " + str(currentFaceID))
+
+                        #Create and store the tracker 
+                        tracker = dlib.correlation_tracker()
+                        tracker.start_track(baseImage,
+                                            dlib.rectangle( x-10,
+                                                            y-20,
+                                                            x+w+10,
+                                                            y+h+20))
+
+                        faceTrackers[ currentFaceID ] = tracker
+
+                        #Start a new thread that is used to simulate 
+                        #face recognition. This is not yet implemented in this
+                        #version :)
+                        t = threading.Thread( target = doRecognizePerson ,
+                                               args=(faceNames, currentFaceID))
+                        t.start()
+
+                        #Increase the currentFaceID counter
+                        currentFaceID += 1
+
+
+
+
+            #Now loop over all the trackers we have and draw the rectangle
+            #around the detected faces. If we 'know' the name for this person
+            #(i.e. the recognition thread is finished), we print the name
+            #of the person, otherwise the message indicating we are detecting
+            #the name of the person
+            for fid in faceTrackers.keys():
+                tracked_position =  faceTrackers[fid].get_position()
+
+                t_x = int(tracked_position.left())
+                t_y = int(tracked_position.top())
+                t_w = int(tracked_position.width())
+                t_h = int(tracked_position.height())
+
+                cv2.rectangle(resultImage, (t_x, t_y),
+                                        (t_x + t_w , t_y + t_h),
+                                        rectangleColor ,2)
+
+
+                if fid in faceNames.keys():
+                    cv2.putText(resultImage, faceNames[fid] , 
+                                (int(t_x + t_w/2), int(t_y)), 
+                                cv2.FONT_HERSHEY_SIMPLEX,
+                                0.5, (255, 255, 255), 2)
+                else:
+                    cv2.putText(resultImage, "Detecting..." , 
+                                (int(t_x + t_w/2), int(t_y)), 
+                                cv2.FONT_HERSHEY_SIMPLEX,
+                                0.5, (255, 255, 255), 2)
+
+
+
+
+
+
+            #Since we want to show something larger on the screen than the
+            #original 320x240, we resize the image again
+            #
+            #Note that it would also be possible to keep the large version
+            #of the baseimage and make the result image a copy of this large
+            #base image and use the scaling factor to draw the rectangle
+            #at the right coordinates.
+            largeResult = cv2.resize(resultImage,
+                                     (OUTPUT_SIZE_WIDTH,OUTPUT_SIZE_HEIGHT))
+
+            #Finally, we want to show the images on the screen
+            cv2.imshow("base-image", baseImage)
+            cv2.imshow("result-image", largeResult)
+
+
+
+
+    #To ensure we can also deal with the user pressing Ctrl-C in the console
+    #we have to check for the KeyboardInterrupt exception and break out of
+    #the main loop
+    except KeyboardInterrupt as e:
+        pass
+
+    #Destroy any OpenCV windows and exit the application
+    cv2.destroyAllWindows()
+    exit(0)
+
+
+if __name__ == '__main__':
+    detectAndTrackMultipleFaces()