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Changes, hopeflly fixes + debug tools

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This commit is contained in:
Ruben van de Ven 2019-02-07 11:05:41 +01:00
parent 5212e3aab2
commit 61d0418edf
5 changed files with 502 additions and 231 deletions
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20
calibrate-capture.py Normal file
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import cv2
c = cv2.VideoCapture(2)
# if not ding this we only have jittery 10fps
c.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
# set camera resoltion
c.set(3, 1280)
c.set(4, 720)
for i in range(15):
_,im = c.read()
cv2.imwrite('calibrate/left-{:06d}.png'.format(i), im)
cv2.imshow('left', im)
if cv2.waitKey(1000) & 0xFF == ord('q'):
break
c.release()
cv2.destroyAllWindows()

44
calibrate.py Normal file
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@ -0,0 +1,44 @@
import numpy as np
import cv2
import glob
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 50, 0.001)
# criteria=cv2.CALIB_CB_FAST_CHECK
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*7,3), np.float32)
objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('calibrate/*.png')
for fname in images:
print(fname)
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# gray = cv2.resize(gray, (640, 360))
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (3,3),None)
print(ret)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(gray, (7,6), corners2,ret)
cv2.imshow('img',img)
cv2.waitKey(5000)
cv2.destroyAllWindows()
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
print(ret, mtx, dist, rvecs, tvecs)

131
get_coordinates.py Normal file
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@ -0,0 +1,131 @@
import pickle
import numpy as np
metricsSize = [960,600]
screenDrawCorners = np.array([[0,0], [metricsSize[0]-1,0], [0, metricsSize[1]-1], [metricsSize[0]-1,metricsSize[1]-1]])
def create_perspective_transform_matrix(src, dst):
""" Creates a perspective transformation matrix which transforms points
in quadrilateral ``src`` to the corresponding points on quadrilateral
``dst``.
Will raise a ``np.linalg.LinAlgError`` on invalid input.
"""
# See:
# * http://xenia.media.mit.edu/~cwren/interpolator/
# * http://stackoverflow.com/a/14178717/71522
in_matrix = []
for (x, y), (X, Y) in zip(src, dst):
in_matrix.extend([
[x, y, 1, 0, 0, 0, -X * x, -X * y],
[0, 0, 0, x, y, 1, -Y * x, -Y * y],
])
A = np.matrix(in_matrix, dtype=np.float)
B = np.array(dst).reshape(8)
af = np.dot(np.linalg.inv(A.T * A) * A.T, B)
m = np.append(np.array(af).reshape(8), 1).reshape((3, 3))
return m
# got this amazing thing from here: https://stackoverflow.com/a/24088499
def create_perspective_transform(src, dst, round=False, splat_args=False):
""" Returns a function which will transform points in quadrilateral
``src`` to the corresponding points on quadrilateral ``dst``::
>>> transform = create_perspective_transform(
... [(0, 0), (10, 0), (10, 10), (0, 10)],
... [(50, 50), (100, 50), (100, 100), (50, 100)],
... )
>>> transform((5, 5))
(74.99999999999639, 74.999999999999957)
If ``round`` is ``True`` then points will be rounded to the nearest
integer and integer values will be returned.
>>> transform = create_perspective_transform(
... [(0, 0), (10, 0), (10, 10), (0, 10)],
... [(50, 50), (100, 50), (100, 100), (50, 100)],
... round=True,
... )
>>> transform((5, 5))
(75, 75)
If ``splat_args`` is ``True`` the function will accept two arguments
instead of a tuple.
>>> transform = create_perspective_transform(
... [(0, 0), (10, 0), (10, 10), (0, 10)],
... [(50, 50), (100, 50), (100, 100), (50, 100)],
... splat_args=True,
... )
>>> transform(5, 5)
(74.99999999999639, 74.999999999999957)
If the input values yield an invalid transformation matrix an identity
function will be returned and the ``error`` attribute will be set to a
description of the error::
>>> tranform = create_perspective_transform(
... np.zeros((4, 2)),
... np.zeros((4, 2)),
... )
>>> transform((5, 5))
(5.0, 5.0)
>>> transform.error
'invalid input quads (...): Singular matrix
"""
try:
transform_matrix = create_perspective_transform_matrix(src, dst)
error = None
except np.linalg.LinAlgError as e:
transform_matrix = np.identity(3, dtype=np.float)
error = "invalid input quads (%s and %s): %s" %(src, dst, e)
error = error.replace("\n", "")
to_eval = "def perspective_transform(%s):\n" %(
splat_args and "*pt" or "pt",
)
to_eval += " res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n"
to_eval += " res = res / res[2]\n"
if round:
to_eval += " return (int(round(res[0][0])), int(round(res[1][0])))\n"
else:
to_eval += " return (res[0][0], res[1][0])\n"
locals = {
"transform_matrix": transform_matrix,
}
locals.update(globals())
exec to_eval in locals, locals
res = locals["perspective_transform"]
res.matrix = transform_matrix
res.error = error
return res
def coordinatesToSrc(coordinates):
return np.array([coordinates['tl'], coordinates['tr'],coordinates['bl'], coordinates['br']])
print("Coordinates in pickle file:")
with open('coordinates.p', 'r') as fp:
c = pickle.load(fp)
for name, coord in c.items():
print("\t", name, coord[0], coord[1])
transform = create_perspective_transform(coordinatesToSrc(c), screenDrawCorners, True)
print("Metrics")
print(metricsSize)
print("Test halfway point:")
x = ((c['tl'][0]+c['bl'][0])/2 + (c['tr'][0]+c['br'][0])/2) / 2
y = ((c['tl'][1]+c['tr'][1])/2 + (c['bl'][1]+c['br'][1])/2) / 2
print("\t",x,y)
print(transform((x,y)))
print("tl", transform((c['tl'])))
print("tr", transform((c['tr'])))
print("bl", transform((c['bl'])))
print("br", transform((c['br'])))

123
head_pose.py
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@ -76,6 +76,11 @@ argParser.add_argument(
default=4,
help="Nr of total processes (min 3)"
)
argParser.add_argument(
'--only-metrics',
action="store_true",
help="Render only metrics instead of the heatmap. Convenient for debugging."
)
args = argParser.parse_args()
@ -88,10 +93,7 @@ logger = logging.getLogger(__name__)
# im = cv2.imread("headPose.jpg");
spotSize = (100,100)
spot = Image.open(os.path.join(cur_dir,"spot.png")).convert('L')
spot = spot.resize(spotSize)
spot = np.array(spot)
predictor_path = os.path.join(cur_dir,"shape_predictor_68_face_landmarks.dat")
@ -106,11 +108,28 @@ screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
# metrics matrix
metricsSize = [1920,1080]
metricsSize = [1280,800]
metricsSize = [960,600]
# metricsSize = [1280,800]
# metricsSize = [960,600]
metricsSize = [1080,1080] # no point in having it different from to the render size
dataframe = pd.DataFrame(columns=['x','y'])
renderSize = [1280,800]
renderSize = [1080,1080]
# Used to create a black backdrop, instead of the ugly Qt-gray, if neccessary
screenSize = [1920,1080]
spotS = int(100./720*renderSize[1])
spotSize = (spotS, spotS)
spot = Image.open(os.path.join(cur_dir,"spot.png")).convert('L')
spot = spot.resize(spotSize)
spot = np.array(spot)
backdrop = None
if screenSize != renderSize:
shape = [screenSize[1],screenSize[0], 3]
backdrop = np.zeros(shape, dtype=np.uint8)
metrics = None
if lastMetricsFilename and os.path.isfile(lastMetricsFilename):
@ -231,7 +250,7 @@ def coordinatesToSrc(coordinates):
# coordinates of the screen boundaries
if os.path.exists("coordinates.p"):
coordinates = pickle.load(open("coordinates.p", "rb"))
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners)
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners, True)
a = [np.array([ 1312.15541183]), np.array([ 244.56278002]), 0]
logger.info("Loaded coordinates: %s", coordinatesToSrc(coordinates))
@ -306,6 +325,7 @@ def captureFacesPoints(i):
# We use this later for calibrating
currentPoint = None
currentVectors = None
currentPoints = []
if len(dets) > 0:
@ -420,13 +440,14 @@ def captureFacesPoints(i):
# 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]
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
currentVectors = {'translation': translation_vector, 'rotation': viewDirectionVector}
currentPoints.append(point)
td3 = time.time()
@ -434,7 +455,7 @@ def captureFacesPoints(i):
# TODO only draw nose line now, so we can change color depending whether on screen or not
results = {'currentPoint': currentPoint, 'currentPoints': currentPoints}
results = {'currentPoint': currentPoint, 'currentPoints': currentPoints, 'currentVectors': currentVectors}
results['im'] = im if not args.hide_preview else None
try:
@ -445,11 +466,14 @@ def captureFacesPoints(i):
def captureVideo():
c = cv2.VideoCapture(args.camera)
# if not ding this we only have jittery 10fps
c.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
# set camera resoltion
# c.set(3, 1280)
# c.set(4, 720)
c.set(3, 960)
c.set(4, 540)
c.set(3, 1280)
c.set(4, 720)
# c.set(3, 960)
# c.set(4, 540)
logger.debug("Camera FPS: {}".format(c.get(5)))
while True:
@ -462,6 +486,7 @@ def captureVideo():
logger.debug("Que sizes: image: {}, points: {} ".format(photoQueue.qsize(), pointsQueue.qsize()))
if __name__ == '__main__':
processes = []
for i in range(args.processes - 2):
p = multiprocessing.Process(target=captureFacesPoints, args=(i,))
@ -479,16 +504,18 @@ lastRunTime = 0
while True:
result = None
try:
te1 = time.time()
try:
result = pointsQueue.get()
te1b = time.time()
im = result['im']
currentPoint = result['currentPoint']
currentPoints = result['currentPoints']
currentVectors = result['currentVectors']
except queue.Empty as e:
logger.warn('Result queue empty')
tr1 = time.time()
if result is not None:
if not args.hide_preview:
# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px)
@ -528,9 +555,13 @@ while True:
targetInt = (int(targetPoint[0]), int(targetPoint[1]))
# check if point fits on screen:
# if so, measure it
if targetInt[0]+spotSize[0] >= 0 and targetInt[1]+spotSize[1] >= 0 and targetInt[0]-spotSize[0] < metricsSize[0] and targetInt[1]-spotSize[0] < metricsSize[1]:
if targetInt[0]+spotSize[0] >= 0 and targetInt[1]+spotSize[1] >= 0 and targetInt[0]-spotSize[0] < metricsSize[0] and targetInt[1]-spotSize[1] < metricsSize[1]:
if not args.hide_graph:
dataframe = dataframe.append({'x':targetInt[0],'y':targetInt[1]}, ignore_index=True)
logger.info("Put metric {},{} in metrix of {},{}".format(targetInt[1],targetInt[0], metricsSize[1], metricsSize[0]))
logger.info("Put metric {},{} in metrics of {},{}".format(targetInt[1],targetInt[0], metricsSize[1], metricsSize[0]))
# newMetrics[targetInt[1]-1,targetInt[0]-1] += 1
#TODO: make it one numpy array action:
for sx in range(spotSize[0]):
for sy in range(spotSize[1]):
@ -539,7 +570,6 @@ while True:
if mx >= 0 and my >= 0 and mx < metricsSize[0] and my < metricsSize[1]:
newMetrics[my,mx] += spot[sx,sy] #/ 20
# print("MAX",np.max(newMetrics))
# after we collected all new metrics, blur them foor smoothness
@ -555,12 +585,12 @@ while True:
if result is not None and not args.hide_preview:
cv2.imshow("Output", im)
te3 = time.time()
logger.debug("Pre processing took: {}s".format(te2-tr1))
logger.debug("showed webcam image in %fs", te3-te2)
logger.debug("Rendering took %fs", te3-te1)
logger.debug("Waited took %fs", te1b-te1)
# blur smooth the heatmap
# logger.debug("Max blurred metrics: %f", np.max(metrics))
@ -568,7 +598,6 @@ while True:
tm21 = time.time()
t = tm21
diffT = min(1, t - lastRunTime)
lastRunTime = t
# animDuration = 1
@ -581,15 +610,65 @@ while True:
# smooth impact of first hits by having at least 0.05
normalisedMetrics = metrics / (max(255*7 ,np.max(metrics)))
# convert to colormap, thanks to: https://stackoverflow.com/a/10967471
if args.only_metrics:
# output only metrics instead of heatmap. Usefull for debugging O:)
nmax = np.max(newMetrics)
renderMetrics = newMetrics/nmax if nmax > 0 else newMetrics
normalisedMetricsColored = np.uint8(renderMetrics *255 )
normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_GRAY2BGR)
# draw grid lines
for i in range(int(metricsSize[0]/100)):
cv2.line(normalisedMetricsColoredBGR, (i*100, 0), (i*100, metricsSize[1]), (150,150,150), 1)
else:
normalisedMetricsColored = np.uint8(cm.nipy_spectral(normalisedMetrics)*255)
normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_RGB2BGR)
if currentPoint is not None and args.verbose:
cv2.putText(normalisedMetricsColoredBGR, "x: {}".format(currentPoint[0]), (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
cv2.putText(normalisedMetricsColoredBGR, "y: {}".format(currentPoint[1]), (10,90), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
cv2.putText(normalisedMetricsColoredBGR, "pos: x: {}, y: {}, z: {}".format(
currentVectors['translation'][0],
currentVectors['translation'][1],
currentVectors['translation'][2]
), (10,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
cv2.putText(
normalisedMetricsColoredBGR,
"rot: x: {}, y: {}, z{}".format(
currentVectors['rotation'][0],
currentVectors['rotation'][1],
currentVectors['rotation'][2],
), (10,130), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
targetPoint = transform(currentPoint)
logger.info("Are we really looking at {}".format(targetPoint))
logger.info("Size: {}".format(normalisedMetricsColoredBGR.shape))
cv2.circle(normalisedMetricsColoredBGR, targetPoint, 2, (0,255,0), -1)
cv2.line(
normalisedMetricsColoredBGR,
(metricsSize[0]/2,metricsSize[1]/2),
tuple(targetPoint),
(255,0,0), 2
)
# cv2.putText(normalisedMetricsColoredBGR, "z: {}".format(currentPoint[2]), (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
tm22 = time.time()
logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics))
# logger.info(normalisedMetrics)
tm23 = time.time()
cv2.imshow("test",normalisedMetricsColoredBGR)
# normalisedMetricsColoredBGR = cv2.resize(normalisedMetricsColoredBGR, tuple(renderSize))
if backdrop is not None:
dx = (screenSize[0] - renderSize[0]) / 2
dy = (screenSize[1] - renderSize[1]) / 2
print(dx, dy)
backdrop[dy:dy+renderSize[1], dx:dx+renderSize[0]] = normalisedMetricsColoredBGR
renderImage = backdrop
else:
renderImage = normalisedMetricsColoredBGR
cv2.imshow("test", renderImage)
# image = Image.fromarray(normalisedMetricsColored)
# wpercent = (imageWindowSize[0] / float(image.size[0]))
# hsize = int((float(image.size[1]) * float(wpercent)))
@ -607,7 +686,7 @@ while True:
# imagesprite = imageCanvas.create_image(renderSize[0]/2, renderSize[1]/2,image=tkpi, tags="IMG")
# imageWindowRoot.update()
tm24 = time.time()
logger.debug("PIL image generated in %fs", tm24 - tm23)
logger.debug("Render in generated in %fs", tm24 - tm23)
# logger.debug("Total matrix time is %fs", tm4 - tm3 + tm2 - tm1 + tm24 - tm21)
if not args.hide_graph:
@ -677,7 +756,7 @@ while True:
pickle.dump( coordinates, open( "coordinates.p", "wb" ) )
logger.info("Saved coordinates")
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners)
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners, True)
duration = time.time()-te1
fps = 1/duration

3
output/.gitignore vendored
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@ -1,3 +0,0 @@
*
!.gitignore