def getUserPicture(outputWidth):
camera = cv2.VideoCapture(0)
if not camera.isOpened():
logging.error("Arrgghhh! The camera is not working!")
return None
outputSize = calculateScaledSize(outputWidth, capture=camera)
logging.debug("Reading camera...")
readOk, image = camera.read()
picWin = "Sonria..."
cv2.namedWindow(picWin)
key = -1
while key != ENTER_KEY and readOk:
image = cv2.resize(image, outputSize)
drawLabel("Presione [Enter]...", image, (int(outputWidth/3), 50))
cv2.imshow(picWin, image)
key = cv2.waitKey(5) % 256
readOk, image = camera.read()
cv2.destroyWindow(picWin)
cv2.waitKey(1)
logging.debug('Picture taken.')
return image
def recognizeVideo(faceRecognizer, videoFileName, subjects, haarFolder):
faceCascade = loadCascadeClassifier(haarFolder + "/haarcascade_frontalface_alt2.xml")
leftEyeCascade = loadCascadeClassifier(haarFolder + "/haarcascade_lefteye_2splits.xml")
rightEyeCascade = loadCascadeClassifier(haarFolder + "/haarcascade_righteye_2splits.xml")
if not videoFileName:
videoFileName = 0
capture = cv2.VideoCapture(videoFileName)
readOk, image = capture.read()
if readOk:
height, width, channels = image.shape
else:
logging.warning("Could not read capture!!")
return
minFaceSize = (int(width * 0.1), int(width * 0.1))
rectColor = (255, 0, 0)
rectThickness = 2
fontColor = (255, 255, 255)
fontScale = 0.8
fontThickness = 1
title = 'Face Recognizer App'
cv2.namedWindow(title)
while cv2.waitKey(10) == -1 and readOk:
faces = detectFaces(image, faceCascade, leftEyeCascade, rightEyeCascade, minFaceSize)
if len(faces) == 0 :
for i in xrange(0, 3):
cv2.imshow(title, image)
_, image = capture.read()
else:
for (x, y, w, h, _, _) in faces:
face = cv2.cvtColor(image[y:y+h, x:x+w], cv2.COLOR_BGR2GRAY)
faceGray = cv2.resize(face, (92, 112))
(prediction, distance) = faceRecognizer.predict(faceGray)
if distance > 140:
predictionLegend = "Unknow subject"
else:
predictionLegend = "Predicted {0} - Distance {1}".format(subjects[prediction], distance)
cv2.rectangle(image, (x,y), (x+w,y+h), rectColor, rectThickness)
drawLabel(predictionLegend, image, (x-20, y-10))
cv2.imshow(title, image)
readOk, image = capture.read()
cv2.destroyWindow(title)
def main():
args = configureArguments()
configureLogging(args.log)
windowTitle = "Test draw app"
cv2.namedWindow(windowTitle)
haarFolder = "/home/juan/ciberpunks/opencv-2.4.11/data/haarcascades"
faceCascade = loadCascadeClassifier(haarFolder + "/haarcascade_frontalface_alt2.xml")
leftEyeCascade = loadCascadeClassifier(haarFolder + "/haarcascade_lefteye_2splits.xml")
rightEyeCascade = loadCascadeClassifier(haarFolder + "/haarcascade_righteye_2splits.xml")
mouthCascade = loadCascadeClassifier(haarFolder + '/haarcascade_mcs_mouth.xml')
color = (120,120,130)
thickness = 2
width = 600
image = cv2.imread('/home/juan/ciberpunks/faces/news/[email protected]')
image = cv2.resize(image, calculateScaledSize(width, image=image))
if image is None:
print 'ERROR: no se pudo leer la imagen.'
return
minFaceSize = (10, 10)
minEyeSize = (5, 5)
faces = detectFaces(image, faceCascade, leftEyeCascade, rightEyeCascade, minFaceSize, minEyeSize)
for (x, y, w, h, leftEyes, rightEyes) in faces:
center = calculateCenter((x,y,w,h))
cv2.line(image, (x,0), (x, width), color, 2)
cv2.line(image, (x+w,0), (x+w, width), color, 2)
cv2.line(image, (0,y), (width, y), color, 2)
cv2.line(image, (0,y+h), (width, y+h), color, 2)
drawLabel("Juan Gabriel", image, (x, y+20))
cv2.imshow(windowTitle, image)
cv2.waitKey(6000)
cv2.destroyWindow(windowTitle)
def draw_boxes(img, boxes, names):
""" Draws bounding boxes of objects detected on given image """
h, w = img.shape[:2]
for box, tid in zip(boxes, names):
# draw rectangle
x, y, w, h = box
x, y, w, h = int(x), int(y), int(w), int(h)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "%d" % (tid)
img = common.drawLabel(img, text, (x, y))
return img
def demo_video(video_file):
detector = ObjectDetectorYolo(model='tiny-yolo-voc')
mtracker = KalmanTracker(['person'], tracker='deep_sort')
cap = common.VideoStream(video_file, queueSize=4).start()
cv2.waitKey(500)
Outcount, Incount = 0, 0
total_t, counter = 0, 0
while not cap.stopped:
t = common.clock()
imgcv = cap.read()
if imgcv is not None:
counter += 1
detections = detector.run(imgcv)
mtracker.update(imgcv, detections)
cvboxes, ids = [], []
for tid, tracker in mtracker.trackers.iteritems():
if tracker.consecutive_invisible_count < 5:
state_current = get_pos(tracker.bbox)
try:
if state_current != tracker.regionside:
tracker.statechange += 1
print state_current, tracker.regionside, tracker.statechange
if state_current == 'Positive':
if tracker.statechange % 2:
Incount += 1
else:
Outcount -= 1
else:
if tracker.statechange % 2:
Outcount += 1
else:
Incount -= 1
tracker.regionside = state_current
except AttributeError:
tracker.regionside = state_current
tracker.statechange = 0
cvboxes.append(tracker.bbox)
ids.append(tid)
print Incount, Outcount
cv2.line(imgcv, (LINE['x1'], LINE['y1']), (LINE['x2'], LINE['y2']),
(0, 0, 255), 4)
common.drawLabel(imgcv,
"IN:%d OUT:%d" % (Incount, Outcount), (10, 10),
size=1,
color=(0, 0, 255))
common.showImage(draw_boxes(imgcv, cvboxes, ids))
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
t1 = common.clock()
dt = t1 - t
t = t1
total_t += dt
print counter / total_t
def demo_video(video_file):
facedemo = Face(detector_method=DETECTOR, recognition_method=None)
mtracker = MultiTracker(SingleTrackerType=CorrelationTracker)
# mtracker = MultiTracker(SingleTrackerType=CorrelationTracker,
# removalConfig=removalConfig)
# mtracker = MultiTracker(SingleTrackerType = cv2.TrackerKCF_create)
cap = common.VideoStream(video_file, queueSize=4).start()
cv2.waitKey(500)
Outcount, Incount = 0, 0
while not cap.stopped:
t = common.clock()
total_t, counter = 0, 0
imgcv = cap.read()
if imgcv is not None:
counter += 1
detections = facedemo.detect(imgcv, upsamples=0)
mtracker.update(imgcv, common.toCvbox(detections))
cvboxes, ids = [], []
for tid, tracker in mtracker.trackers.items():
if tracker.visible_count > 3 and tracker.consecutive_invisible_count < 10:
state_current = get_pos(tracker.bbox)
try:
if state_current != tracker.regionside:
tracker.statechange += 1
print state_current, tracker.regionside, tracker.statechange
if state_current == 'Positive':
if tracker.statechange % 2:
Incount += 1
else:
Outcount -= 1
else:
if tracker.statechange % 2:
Outcount += 1
else:
Incount -= 1
tracker.regionside = state_current
except AttributeError:
tracker.regionside = state_current
tracker.statechange = 0
cvboxes.append(tracker.bbox)
ids.append(tid)
detections = to_bbox(cvboxes)
print Incount, Outcount
cv2.line(imgcv, (LINE['x1'], LINE['y1']), (LINE['x2'], LINE['y2']),
(0, 0, 255), 4)
imgcv = common.drawLabel(imgcv,
"IN:%d OUT:%d" % (Incount, Outcount),
(10, 10),
color=(0, 0, 255))
common.showImage(common.drawObjects(imgcv, detections, ids))
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
t1 = common.clock()
dt = t1 - t
t = t1
total_t += dt
print counter / total_t