First working multiprocessing

This commit is contained in:
Ruben van de Ven 2019-02-06 00:42:22 +01:00
parent 02627cf897
commit fd76f7a97d
1 changed files with 212 additions and 159 deletions

View File

@ -21,9 +21,11 @@ else:
import tkinter as Tk
import time
import datetime
import Queue
import coloredlogs
import argparse
import multiprocessing
argParser = argparse.ArgumentParser(description='Draw a heatmap')
argParser.add_argument(
@ -65,6 +67,12 @@ argParser.add_argument(
default=0,
help="Nr of frames to keep in queue (adds a delay)"
)
argParser.add_argument(
'--processes',
type=int,
default=4,
help="Nr of total processes (min 3)"
)
args = argParser.parse_args()
@ -75,14 +83,6 @@ coloredlogs.install(
logger = logging.getLogger(__name__)
# Read Image
#c = cv2.VideoCapture(args.camera)
c = cv2.VideoCapture(args.camera)
# set camera resoltion
c.set(3, 1280)
c.set(4, 720)
#c.set(3, 480)
#c.set(4, 320)
# im = cv2.imread("headPose.jpg");
@ -93,8 +93,6 @@ if args.output_dir:
else:
lastMetricsFilename = None
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
@ -271,154 +269,206 @@ if args.output_dir:
if args.queue_length:
imageQueue = []
lock = multiprocessing.Lock()
photoQueue = multiprocessing.Queue(maxsize=args.processes)
pointsQueue = multiprocessing.Queue(maxsize=args.processes)
def captureFacesPoints(i):
logger.info("Start capturer {}".format( i))
# dedicated detector & predictor instances:
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
while True:
t1 = time.time()
im = photoQueue.get(block=True, timeout=10)
if im is None:
continue
logger.debug("Got foto in {}".format( i))
size = im.shape
t2 = time.time()
logger.debug("Captured frame in %fs", t2-t1)
# Docs: Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(im, 1)
t3 = time.time()
logger.debug("Number of faces detected: {} - took {}s".format(len(dets), t3-t2))
# We use this later for calibrating
currentPoint = None
currentPoints = []
if len(dets) > 0:
for d in dets:
td1 = time.time()
shape = predictor(im, d)
td2 = time.time()
logger.debug("Found face points in %fs", td2-td1)
#2D image points. If you change the image, you need to change vector
image_points = np.array([
(shape.part(30).x,shape.part(30).y), # Nose tip
(shape.part(8).x,shape.part(8).y), # Chin
(shape.part(36).x,shape.part(36).y), # Left eye left corner
(shape.part(45).x,shape.part(45).y), # Right eye right corne
(shape.part(48).x,shape.part(48).y), # Left Mouth corner
(shape.part(54).x,shape.part(54).y) # Right mouth corner
], dtype="double")
# 3D model points.
model_points = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corne
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
])
# Camera internals
focal_length = size[1]
center = (size[1]/2, size[0]/2)
camera_matrix = np.array(
[[focal_length, 0, center[0]],
[0, focal_length, center[1]],
[0, 0, 1]], dtype = "double"
)
# logger.info ("Camera Matrix :\n {0}".format(camera_matrix))
dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion
(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)
if not success:
logger.info("Error determening PnP {}".format(success) )
continue
logger.debug ("Rotation Vector:\n %s", rotation_vector)
logger.debug ("Translation Vector:\n {0}".format(translation_vector))
# Project a 3D point (0, 0, 1000.0) onto the image plane.
# We use this to draw a line sticking out of the nose
(nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)
for p in image_points:
cv2.circle(im, (int(p[0]), int(p[1])), 3, (0,0,255), -1)
p1 = ( int(image_points[0][0]), int(image_points[0][1]))
p2 = ( int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
cv2.line(im, p1, p2, (255,0,0), 2)
rotMatrix = np.zeros([3,3])
cv2.Rodrigues(rotation_vector, rotMatrix, jacobian=0)
# Find rotation: https://stackoverflow.com/a/15029416
# not used anymore :-)
# rx = np.arctan2(rotMatrix[2,1], rotMatrix[2,2])
# ry = np.arctan2(-rotMatrix[2,0], np.sqrt(np.square(rotMatrix[2,1]) + np.square(rotMatrix[2,2])))
# rz = np.arctan2(rotMatrix[1,0],rotMatrix[0,0])
# logger.info("rotation {} {} {}".format(rx, ry, rz) )
# ry = - np.arcsin(rotMatrix[0,2])
# rx = np.arctan2(rotMatrix[1,2]/np.cos(ry), rotMatrix[2,2]/np.cos(ry))
# rz = np.arctan2(rotMatrix[0,1]/np.cos(ry), rotMatrix[0,0]/np.cos(ry))
# logger.info("rotation ml {} {} {}".format(rx, ry, rz) )# seems better?
viewDirectionVector = np.dot(np.array([0.0, 0.0, 1.0]), rotMatrix)
if not args.hide_preview:
# draw little floorplan for x: 10 -> 50 maps to z: 0 -> 10000, x: -2000 -> 2000
mapPosX = int((translation_vector[0] + 500) / 1000 * 40)
mapPosY = int((translation_vector[1] + 500) / 1000 * 40)
mapPosZ = int((translation_vector[2] + 0 ) / 10000 * 40)
cv2.circle(im, (mapPosZ + 10, mapPosX + 10), 2, (0,0,255), -1)
cv2.circle(im, (mapPosZ + 60, mapPosY + 10), 2, (0,0,255), -1)
# make it an _amazing_ stick figurine for the side view
cv2.line(im, (mapPosZ + 60, mapPosY + 10), (mapPosZ + 60, mapPosY + 20), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 20), (mapPosZ + 55, mapPosY + 25), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 20), (mapPosZ + 65, mapPosY + 25), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 15), (mapPosZ + 55, mapPosY + 10), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 15), (mapPosZ + 65, mapPosY + 10), (0,0,255), 1)
# draw rotation vector
cv2.circle(im, (mapPosZ + 60, mapPosY + 10), 2, (0,0,255), -1)
cv2.line(im, (mapPosZ + 10, mapPosX + 10), (mapPosZ + 10 + int(viewDirectionVector[2] * 100), mapPosX + 10 + int(viewDirectionVector[0] * 100)), (255,255,0), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 10), (mapPosZ + 60 + int(viewDirectionVector[2] * 100), mapPosY + 10 - int(viewDirectionVector[1] * 100)), (255,0,255), 1)
# Translation vector gives position in space:
# x, y z: 0,0,0 is center of camera
# line: (x,y,z) = f(a) = (t1 + r1*a, t2+r2*a, t3 + r3*a)
# Screen: (x,y,z) = (x,y,0)
# Interesection:
# x = t1 + r1 * a
# y = t2 + r2 * a
# z = t3 * r3 * a = 0
# => a = -t3 / r3
# substitute found a in x,y
# seems to be wrong?
# a = - translation_vector[2]# / rotation_vector[2]
# x = translation_vector[0] + rotation_vector[0] * a
# y = translation_vector[1] + rotation_vector[1] * a
# logger.warn("First {} {},{}".format(a,x,y))
a = - translation_vector[2]# / viewDirectionVector[2]
x = translation_vector[0] + viewDirectionVector[0] * a
y = translation_vector[1] + viewDirectionVector[1] * a
# logger.warn("Second {} {},{}".format(a,x,y))
point = np.array([x,y])
currentPoint = point
currentPoints.append(point)
td3 = time.time()
logger.debug("Timer: All other face drawing stuff in %fs", td3-td2)
# TODO only draw nose line now, so we can change color depending whether on screen or not
results = {'currentPoint': currentPoint, 'currentPoints': currentPoints, 'im': im}
try:
pointsQueue.put_nowait(results)
except Queue.Full as e:
logger.critical("Reslt queue full?")
# not applicable to multiprocessing.queue in p2.7: photoQueue.task_done()
def captureVideo():
c = cv2.VideoCapture(args.camera)
# set camera resoltion
c.set(3, 1280)
c.set(4, 720)
logger.debug("Camera FPS: {}".format(c.get(5)))
while True:
_, im = c.read()
try:
photoQueue.put_nowait(im)
except Queue.Full as e:
logger.debug("Photo queue full")
time.sleep(.05)
logger.debug("Que sizes: image: {}, points: {} ".format(photoQueue.qsize(), pointsQueue.qsize()))
processes = []
for i in range(args.processes - 2):
p = multiprocessing.Process(target=captureFacesPoints, args=(i,))
p.daemon = True
p.start()
processes.append(p)
p = multiprocessing.Process(target=captureVideo, args=())
p.daemon = True
p.start()
processes.append(p)
while True:
if args.hide_preview:
# if preview is hidden, we can always re-raise the image window
imageWindowRoot.lift()
t1 = time.time()
_, im = c.read()
size = im.shape
t2 = time.time()
logger.debug("Captured frame in %fs", t2-t1)
# Docs: Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(im, 1)
t3 = time.time()
logger.debug("Number of faces detected: {} - took {}s".format(len(dets), t3-t2))
# We use this later for calibrating
currentPoint = None
currentPoints = []
if len(dets) > 0:
for d in dets:
td1 = time.time()
shape = predictor(im, d)
td2 = time.time()
logger.debug("Found face points in %fs", td2-td1)
#2D image points. If you change the image, you need to change vector
image_points = np.array([
(shape.part(30).x,shape.part(30).y), # Nose tip
(shape.part(8).x,shape.part(8).y), # Chin
(shape.part(36).x,shape.part(36).y), # Left eye left corner
(shape.part(45).x,shape.part(45).y), # Right eye right corne
(shape.part(48).x,shape.part(48).y), # Left Mouth corner
(shape.part(54).x,shape.part(54).y) # Right mouth corner
], dtype="double")
# 3D model points.
model_points = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corne
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
])
# Camera internals
focal_length = size[1]
center = (size[1]/2, size[0]/2)
camera_matrix = np.array(
[[focal_length, 0, center[0]],
[0, focal_length, center[1]],
[0, 0, 1]], dtype = "double"
)
# logger.info ("Camera Matrix :\n {0}".format(camera_matrix))
dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion
(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)
if not success:
logger.info("Error determening PnP {}".format(success) )
continue
logger.debug ("Rotation Vector:\n %s", rotation_vector)
logger.info ("Translation Vector:\n {0}".format(translation_vector))
# Project a 3D point (0, 0, 1000.0) onto the image plane.
# We use this to draw a line sticking out of the nose
(nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)
for p in image_points:
cv2.circle(im, (int(p[0]), int(p[1])), 3, (0,0,255), -1)
p1 = ( int(image_points[0][0]), int(image_points[0][1]))
p2 = ( int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
cv2.line(im, p1, p2, (255,0,0), 2)
rotMatrix = np.zeros([3,3])
cv2.Rodrigues(rotation_vector, rotMatrix, jacobian=0)
# Find rotation: https://stackoverflow.com/a/15029416
# not used anymore :-)
# rx = np.arctan2(rotMatrix[2,1], rotMatrix[2,2])
# ry = np.arctan2(-rotMatrix[2,0], np.sqrt(np.square(rotMatrix[2,1]) + np.square(rotMatrix[2,2])))
# rz = np.arctan2(rotMatrix[1,0],rotMatrix[0,0])
# logger.info("rotation {} {} {}".format(rx, ry, rz) )
# ry = - np.arcsin(rotMatrix[0,2])
# rx = np.arctan2(rotMatrix[1,2]/np.cos(ry), rotMatrix[2,2]/np.cos(ry))
# rz = np.arctan2(rotMatrix[0,1]/np.cos(ry), rotMatrix[0,0]/np.cos(ry))
# logger.info("rotation ml {} {} {}".format(rx, ry, rz) )# seems better?
viewDirectionVector = np.dot(np.array([0.0, 0.0, 1.0]), rotMatrix)
if not args.hide_preview:
# draw little floorplan for x: 10 -> 50 maps to z: 0 -> 10000, x: -2000 -> 2000
mapPosX = int((translation_vector[0] + 500) / 1000 * 40)
mapPosY = int((translation_vector[1] + 500) / 1000 * 40)
mapPosZ = int((translation_vector[2] + 0 ) / 10000 * 40)
cv2.circle(im, (mapPosZ + 10, mapPosX + 10), 2, (0,0,255), -1)
cv2.circle(im, (mapPosZ + 60, mapPosY + 10), 2, (0,0,255), -1)
# make it an _amazing_ stick figurine for the side view
cv2.line(im, (mapPosZ + 60, mapPosY + 10), (mapPosZ + 60, mapPosY + 20), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 20), (mapPosZ + 55, mapPosY + 25), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 20), (mapPosZ + 65, mapPosY + 25), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 15), (mapPosZ + 55, mapPosY + 10), (0,0,255), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 15), (mapPosZ + 65, mapPosY + 10), (0,0,255), 1)
# draw rotation vector
cv2.circle(im, (mapPosZ + 60, mapPosY + 10), 2, (0,0,255), -1)
cv2.line(im, (mapPosZ + 10, mapPosX + 10), (mapPosZ + 10 + int(viewDirectionVector[2] * 100), mapPosX + 10 + int(viewDirectionVector[0] * 100)), (255,255,0), 1)
cv2.line(im, (mapPosZ + 60, mapPosY + 10), (mapPosZ + 60 + int(viewDirectionVector[2] * 100), mapPosY + 10 - int(viewDirectionVector[1] * 100)), (255,0,255), 1)
# Translation vector gives position in space:
# x, y z: 0,0,0 is center of camera
# line: (x,y,z) = f(a) = (t1 + r1*a, t2+r2*a, t3 + r3*a)
# Screen: (x,y,z) = (x,y,0)
# Interesection:
# x = t1 + r1 * a
# y = t2 + r2 * a
# z = t3 * r3 * a = 0
# => a = -t3 / r3
# substitute found a in x,y
# seems to be wrong?
a = - translation_vector[2]# / rotation_vector[2]
x = translation_vector[0] + rotation_vector[0] * a
y = translation_vector[1] + rotation_vector[1] * a
logger.warn("First {} {},{}".format(a,x,y))
a = - translation_vector[2]# / viewDirectionVector[2]
x = translation_vector[0] + viewDirectionVector[0] * a
y = translation_vector[1] + viewDirectionVector[1] * a
logger.warn("Second {} {},{}".format(a,x,y))
point = np.array([x,y])
currentPoint = point
currentPoints.append(point)
td3 = time.time()
logger.debug("Timer: All other face drawing stuff in %fs", td3-td2)
# TODO only draw nose line now, so we can change color depending whether on screen or not
# processed all faces, now draw on screen:
te1 = time.time()
result = pointsQueue.get()
im = result['im']
currentPoint = result['currentPoint']
currentPoints = result['currentPoints']
if not args.hide_preview:
# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px)
@ -469,17 +519,18 @@ while True:
# after we collected all new metrics, blur them foor smoothness
# and add to all metrics collected
tm3 = time.time()
metrics = metrics + gaussian_filter(newMetrics, sigma = 13)
# metrics = metrics + gaussian_filter(newMetrics, sigma = 13)
metrics = metrics + newMetrics
tm4 = time.time()
logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
# Display webcam image with overlays
te2 = time.time()
logger.debug("Drew on screen in %fs", te2-te1)
if not args.hide_preview:
cv2.imshow("Output", im)
te3 = time.time()
logger.debug("showed webcam image in %fs", te3-te2)
logger.debug("Rendering took %fs", te3-te1)
# blur smooth the heatmap
# logger.debug("Max blurred metrics: %f", np.max(metrics))
@ -584,6 +635,8 @@ while True:
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners)
duration = time.time()-te1
fps = 1/duration
logger.info("Rendering loop %fs %ffps", duration, fps)
cv2.destroyAllWindows()