def main():
detectum = dnn.Detector()
THRESHOLD = 0.4 # Value between 0 and 1 for confidence score
im = cv.imread('../data/beatles.jpg')
_, bboxes = detectum.process_frame(im, THRESHOLD)
# --- uncomment to utilise post-processing --- #
# 1.
# Post-processing to remove any bboxes whose coordinates are inside larger bboxes.
# bboxes = remove_inside_bboxes(bboxes) # uncomment me!!!
# 2.
# Post-processing that combines any two overlapping bboxes into one larger bbox.
# bboxes = combine_bboxes(bboxes) # uncomment me!!!
# Loop through list (if empty this will be skipped) and overlay green bboxes
# Format of bboxes is: xmin, ymin (top left), xmax, ymax (bottom right)
for i in bboxes:
cv.rectangle(im, (i[0], i[1]), (i[2], i[3]), (0, 255, 0), 3)
cv.imwrite('../data/beatles_output.jpg', im)
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ # This script has been tested using a Raspberry Pi Camera Modeule v1.3 from picamera.array import PiRGBArray from picamera import PiCamera import time import cv2 as cv import math from xailient import dnn import numpy as np #By default Low resolution DNN for face detector will be loaded. #To load the high resolution Face detector please comment the below lines. detectum = dnn.Detector() THRESHOLD = 0.45 # Value between 0 and 1 for confidence score # initialize the camera and grab a reference to the raw camera capture RES_W = 640 # 1280 # 640 # 256 # 320 # 480 # pixels RES_H = 480 # 720 # 480 # 144 # 240 # 360 # pixels camera = PiCamera() camera.resolution = (RES_W, RES_H) camera.framerate = 30 # FPS rawCapture = PiRGBArray(camera, size=(RES_W, RES_H)) # allow the camera to warmup time.sleep(0.1)
def faceTracking(sender):
res1 = (320, 240)
res2 = (480, 320)
res3 = (640, 480)
res4 = (1280, 720)
res = res1
detectum = dnn.Detector()
THRESHOLD = 0.55 # Value between 0 and 1 for confidence score
cap = cv2.VideoCapture(-1)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, res[0])
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, res[1])
frameCounter = 0
currentID = 0
faceTrackers = {}
WIDTH = res[0] / 2
HEIGHT = res[1] / 2
EYE_DEPTH = 2
hFOV = 125 / 2 #62/2
vFOV = 85 / 2 #49/2
ppcm = WIDTH * 2 / 15.5 * 1.5
term = False
while not term:
_, frame = cap.read()
frame = cv2.rotate(frame, cv2.ROTATE_180)
frameCounter += 1
if frameCounter % 1 == 0:
_, bboxes = detectum.process_frame(frame, THRESHOLD)
for (x1, y1, x2, y2) in bboxes:
w = x2 - x1
h = y2 - y1
print(x1, y1, x2, y2)
center = (int(x1 + (x2 - x1) * 0.5), int(y1 + (y2 - y1) * 0.5))
fidMatch = False
for fid in faceTrackers.keys():
(tx1, ty1, tx2, ty2, n, u) = faceTrackers.get(fid)
if tx1 - w * 0.5 <= center[
0] <= tx2 + w * 0.5 and ty1 - h * 0.5 <= center[
1] <= ty2 + h * 0.5:
if n < 50: n += 1
faceTrackers.update({fid: (x1, y1, x2, y2, n, True)})
fidMatch = True
break
if not fidMatch:
faceTrackers.update({currentID: (x1, y1, x2, y2, 1, True)})
currentID += 1
trackID = -1
fidsToDelete = []
for fid in faceTrackers.keys():
(tx1, ty1, tx2, ty2, n, u) = faceTrackers.get(fid)
if not u: n -= 1
if n < 1: fidsToDelete.append(fid)
else:
faceTrackers.update({fid: (tx1, ty1, tx2, ty2, n, False)})
if n < 25:
pass
else:
trackID = fid
for fid in fidsToDelete:
faceTrackers.pop(fid, None)
print(faceTrackers)
if trackID != -1:
# determine who to track
(x1, y1, x2, y2, n, u) = faceTrackers.get(trackID)
center = (int(x1 + (x2 - x1) * 0.5), int(y1 + (y2 - y1) * 0.5))
hAngle = (1 - center[0] / WIDTH) * hFOV
vAngle = (1 - center[1] / HEIGHT) * vFOV
c = -0.26 * (x2 - x1) + 103
# horizontal
b = 4
angleA = (90 - hAngle) * math.pi / 180
a = math.sqrt(b * b + c * c - 2 * b * c * math.cos(angleA))
angleC = math.acos((a * a + b * b - c * c) / (2 * a * b))
pupilL = int((angleC - math.pi / 2) * EYE_DEPTH * ppcm)
b_hat = 2 * b
c_hat = math.sqrt(a * a + b_hat * b_hat -
2 * a * b_hat * math.cos(angleC))
angleA_hat = math.acos(
(b_hat * b_hat + c_hat * c_hat - a * a) / (2 * b_hat * c_hat))
pupilR = int((math.pi / 2 - angleA_hat) * EYE_DEPTH * ppcm)
# vertical
b = 6
angleA = (90 - vAngle) * math.pi / 180
a = math.sqrt(b * b + c * c - 2 * b * c * math.cos(angleA))
angleC = math.acos((a * a + b * b - c * c) / (2 * a * b))
pupilV = int((angleC - math.pi / 2) * EYE_DEPTH * ppcm)
sender.send((pupilL, pupilR, pupilV))
if sender.poll():
term = sender.recv()
cap.release()