def whichPerson(img):
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
personROI = convertForKeras(img)
if len(people) == 0:
newPerson = Person(0)
newPerson.addPrevious(personROI)
people.append(newPerson)
return newPerson.getIdentifier()
else:
closest = 100
closestPerson = 100
closestFrame = None
save = False
for person in people:
print(
str(person.getIdentifier()) + " activity is " +
str(person.isActive()))
if not person.isActive():
save = True
currentPerson = person.getIdentifier()
previous = person.getPrevious()
for previousFrame in previous:
prediction = queryNeuralNetwork(personROI, previousFrame)
print("person " + str(person.getIdentifier()) +
" distance = " + str(prediction))
if prediction < closest:
closest = prediction
closestPerson = currentPerson
closestFrame = previousFrame
# if save:
# concat = np.concatenate((personROI, closestFrame), axis=1)
# concat *= 255
# cv2.imwrite("classificationsREID/"+str(closest)+" "+str(closestPerson)+".jpg",concat)
# count the number of times these are different people and <0.5, or the same person and >0.5
if closest < 0.5:
person = people[closestPerson]
person.addPrevious(personROI)
return person.getIdentifier()
else:
nextPerson = Person(len(people))
nextPerson.addPrevious(personROI)
people.append(nextPerson)
return nextPerson.getIdentifier()
def runOnSingleCamera(video_file):
cap.open(video_file)
# create window by name (as resizable)
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
cv2.resizeWindow(windowName, 640, 480)
# set up HoG detector
mog = cv2.createBackgroundSubtractorMOG2(history=2000,
varThreshold=16,
detectShadows=True)
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
keep_processing = True
previousImg = np.zeros((640, 480, 3), np.uint8)
while (keep_processing):
ret, img = cap.read()
if not is_similar(img, previousImg):
displayImage = img
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
fgmask = mog.apply(img)
fgthres = cv2.threshold(fgmask.copy(), 200, 255,
cv2.THRESH_BINARY)[1]
fgdilated = cv2.dilate(fgthres,
kernel=cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (3, 3)),
iterations=3)
_, contours, hierarchy = cv2.findContours(fgthres,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for i in contours:
x, y, w, h = cv2.boundingRect(i)
originalx = x
originaly = y
originalw = w
originalh = h
x = int(max(0, x - padding / 100.0 * w))
y = int(max(0, y - padding / 100.0 * h))
w = int(min(img.shape[1] - 1, (w + 2 * padding / 100.0 * w)))
h = int(min(img.shape[0] - 1, (h + 2 * padding / 100.0 * h)))
if ((w >= width) and (h >= height) and (x + w < img.shape[1])
and (y + h < img.shape[0])):
#cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
roi = img[y:h + y, x:w + x]
#perform HOG based pedestrain detection
found, w = hog.detectMultiScale(roi,
winStride=(8, 8),
padding=(8, 16),
scale=1.05)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
#draw_detections(img, found)
# draw_detections(displayImage, found_filtered, colour, 3)
for x, y, w, h in found_filtered:
#w, h = int(0.15*w), int(0.05*h)
personROI = roi[y:h + y, x:w + x]
personROI = convertForKeras(personROI)
if len(people) == 0:
newPerson = Person(0)
newPerson.addPrevious(personROI)
people.append(newPerson)
draw_detections(displayImage, originalx, originaly,
originalw, originalh,
newPerson.getColour(), 3)
else:
closest = 100
closestPerson = 100
for person in people:
currentPerson = person.getIdentifier()
previous = person.getPrevious()
for previousFrame in previous:
prediction = queryNeuralNetwork(
personROI, previousFrame)
print(currentPerson, prediction)
if prediction < closest:
closest = prediction
closestPerson = currentPerson
if closest < 0.5:
person = people[closestPerson]
person.addPrevious(personROI)
draw_detections(displayImage, originalx,
originaly,
originalw, originalh,
person.getColour(), 3)
print("REID")
else:
newPerson = Person(len(people))
newPerson.addPrevious(personROI)
people.append(newPerson)
draw_detections(displayImage, originalx,
originaly,
originalw, originalh,
newPerson.getColour(), 3)
print("NEW")
# display image
cv2.imshow(windowName, displayImage)
previousImg = img
# if user presses "x" then exit
key = cv2.waitKey(
1) & 0xFF # wait 200ms (i.e. 1000ms / 5 fps = 200 ms)
if (key == ord('x')):
keep_processing = False
# close all windows
cv2.destroyAllWindows()
