Changes, hopeflly fixes + debug tools
Ruben van de Ven
parent
5212e3aab2
commit
61d0418edf
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@ -0,0 +1,20 @@
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import cv2
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c = cv2.VideoCapture(2)
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# if not ding this we only have jittery 10fps
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c.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
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# set camera resoltion
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c.set(3, 1280)
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c.set(4, 720)
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for i in range(15):
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_,im = c.read()
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cv2.imwrite('calibrate/left-{:06d}.png'.format(i), im)
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cv2.imshow('left', im)
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if cv2.waitKey(1000) & 0xFF == ord('q'):
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break
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c.release()
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cv2.destroyAllWindows()
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@ -0,0 +1,44 @@
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import numpy as np
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import cv2
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import glob
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# termination criteria
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 50, 0.001)
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# criteria=cv2.CALIB_CB_FAST_CHECK
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# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
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objp = np.zeros((6*7,3), np.float32)
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objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
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# Arrays to store object points and image points from all the images.
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objpoints = [] # 3d point in real world space
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imgpoints = [] # 2d points in image plane.
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images = glob.glob('calibrate/*.png')
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for fname in images:
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print(fname)
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img = cv2.imread(fname)
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gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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# gray = cv2.resize(gray, (640, 360))
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# Find the chess board corners
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ret, corners = cv2.findChessboardCorners(gray, (3,3),None)
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print(ret)
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# If found, add object points, image points (after refining them)
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if ret == True:
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objpoints.append(objp)
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corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
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imgpoints.append(corners2)
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# Draw and display the corners
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img = cv2.drawChessboardCorners(gray, (7,6), corners2,ret)
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cv2.imshow('img',img)
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cv2.waitKey(5000)
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cv2.destroyAllWindows()
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
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print(ret, mtx, dist, rvecs, tvecs)
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@ -0,0 +1,131 @@
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import pickle
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import numpy as np
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metricsSize = [960,600]
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screenDrawCorners = np.array([[0,0], [metricsSize[0]-1,0], [0, metricsSize[1]-1], [metricsSize[0]-1,metricsSize[1]-1]])
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def create_perspective_transform_matrix(src, dst):
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""" Creates a perspective transformation matrix which transforms points
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in quadrilateral ``src`` to the corresponding points on quadrilateral
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``dst``.
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Will raise a ``np.linalg.LinAlgError`` on invalid input.
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"""
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# See:
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# * http://xenia.media.mit.edu/~cwren/interpolator/
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# * http://stackoverflow.com/a/14178717/71522
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in_matrix = []
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for (x, y), (X, Y) in zip(src, dst):
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in_matrix.extend([
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[x, y, 1, 0, 0, 0, -X * x, -X * y],
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[0, 0, 0, x, y, 1, -Y * x, -Y * y],
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])
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A = np.matrix(in_matrix, dtype=np.float)
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B = np.array(dst).reshape(8)
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af = np.dot(np.linalg.inv(A.T * A) * A.T, B)
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m = np.append(np.array(af).reshape(8), 1).reshape((3, 3))
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return m
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# got this amazing thing from here: https://stackoverflow.com/a/24088499
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def create_perspective_transform(src, dst, round=False, splat_args=False):
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""" Returns a function which will transform points in quadrilateral
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``src`` to the corresponding points on quadrilateral ``dst``::
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... )
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>>> transform((5, 5))
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(74.99999999999639, 74.999999999999957)
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If ``round`` is ``True`` then points will be rounded to the nearest
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integer and integer values will be returned.
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... round=True,
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... )
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>>> transform((5, 5))
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(75, 75)
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If ``splat_args`` is ``True`` the function will accept two arguments
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instead of a tuple.
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... splat_args=True,
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... )
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>>> transform(5, 5)
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(74.99999999999639, 74.999999999999957)
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If the input values yield an invalid transformation matrix an identity
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function will be returned and the ``error`` attribute will be set to a
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description of the error::
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>>> tranform = create_perspective_transform(
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... np.zeros((4, 2)),
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... np.zeros((4, 2)),
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... )
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>>> transform((5, 5))
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(5.0, 5.0)
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>>> transform.error
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'invalid input quads (...): Singular matrix
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"""
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try:
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transform_matrix = create_perspective_transform_matrix(src, dst)
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error = None
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except np.linalg.LinAlgError as e:
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transform_matrix = np.identity(3, dtype=np.float)
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error = "invalid input quads (%s and %s): %s" %(src, dst, e)
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error = error.replace("\n", "")
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to_eval = "def perspective_transform(%s):\n" %(
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splat_args and "*pt" or "pt",
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)
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to_eval += " res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n"
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to_eval += " res = res / res[2]\n"
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if round:
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to_eval += " return (int(round(res[0][0])), int(round(res[1][0])))\n"
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else:
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to_eval += " return (res[0][0], res[1][0])\n"
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locals = {
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"transform_matrix": transform_matrix,
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}
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locals.update(globals())
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exec to_eval in locals, locals
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res = locals["perspective_transform"]
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res.matrix = transform_matrix
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res.error = error
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return res
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def coordinatesToSrc(coordinates):
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return np.array([coordinates['tl'], coordinates['tr'],coordinates['bl'], coordinates['br']])
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print("Coordinates in pickle file:")
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with open('coordinates.p', 'r') as fp:
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c = pickle.load(fp)
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for name, coord in c.items():
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print("\t", name, coord[0], coord[1])
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transform = create_perspective_transform(coordinatesToSrc(c), screenDrawCorners, True)
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print("Metrics")
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print(metricsSize)
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print("Test halfway point:")
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x = ((c['tl'][0]+c['bl'][0])/2 + (c['tr'][0]+c['br'][0])/2) / 2
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y = ((c['tl'][1]+c['tr'][1])/2 + (c['bl'][1]+c['br'][1])/2) / 2
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print("\t",x,y)
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print(transform((x,y)))
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print("tl", transform((c['tl'])))
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print("tr", transform((c['tr'])))
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print("bl", transform((c['bl'])))
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print("br", transform((c['br'])))
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535
head_pose.py
535
head_pose.py
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@ -76,6 +76,11 @@ argParser.add_argument(
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default=4,
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help="Nr of total processes (min 3)"
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)
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argParser.add_argument(
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'--only-metrics',
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action="store_true",
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help="Render only metrics instead of the heatmap. Convenient for debugging."
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)
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args = argParser.parse_args()
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@ -88,10 +93,7 @@ logger = logging.getLogger(__name__)
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# im = cv2.imread("headPose.jpg");
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spotSize = (100,100)
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spot = Image.open(os.path.join(cur_dir,"spot.png")).convert('L')
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spot = spot.resize(spotSize)
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spot = np.array(spot)
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predictor_path = os.path.join(cur_dir,"shape_predictor_68_face_landmarks.dat")
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@ -106,11 +108,28 @@ screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
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# metrics matrix
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metricsSize = [1920,1080]
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metricsSize = [1280,800]
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metricsSize = [960,600]
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# metricsSize = [1280,800]
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# metricsSize = [960,600]
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metricsSize = [1080,1080] # no point in having it different from to the render size
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dataframe = pd.DataFrame(columns=['x','y'])
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renderSize = [1280,800]
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renderSize = [1080,1080]
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# Used to create a black backdrop, instead of the ugly Qt-gray, if neccessary
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screenSize = [1920,1080]
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spotS = int(100./720*renderSize[1])
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spotSize = (spotS, spotS)
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spot = Image.open(os.path.join(cur_dir,"spot.png")).convert('L')
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spot = spot.resize(spotSize)
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spot = np.array(spot)
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backdrop = None
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if screenSize != renderSize:
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shape = [screenSize[1],screenSize[0], 3]
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backdrop = np.zeros(shape, dtype=np.uint8)
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metrics = None
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if lastMetricsFilename and os.path.isfile(lastMetricsFilename):
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@ -231,7 +250,7 @@ def coordinatesToSrc(coordinates):
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# coordinates of the screen boundaries
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if os.path.exists("coordinates.p"):
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coordinates = pickle.load(open("coordinates.p", "rb"))
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transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners)
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transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners, True)
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a = [np.array([ 1312.15541183]), np.array([ 244.56278002]), 0]
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logger.info("Loaded coordinates: %s", coordinatesToSrc(coordinates))
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@ -306,6 +325,7 @@ def captureFacesPoints(i):
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# We use this later for calibrating
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currentPoint = None
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currentVectors = None
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currentPoints = []
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if len(dets) > 0:
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@ -420,13 +440,14 @@ def captureFacesPoints(i):
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# x = translation_vector[0] + rotation_vector[0]* a
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# y = translation_vector[1] + rotation_vector[1] * a
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# logger.warn("First {} {},{}".format(a,x,y))
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a = - translation_vector[2]# / viewDirectionVector[2]
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a = translation_vector[2] / viewDirectionVector[2]
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x = translation_vector[0] + viewDirectionVector[0] * a
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y = translation_vector[1] + viewDirectionVector[1] * a
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# logger.warn("Second {} {},{}".format(a,x,y))
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point = np.array([x,y])
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currentPoint = point
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currentVectors = {'translation': translation_vector, 'rotation': viewDirectionVector}
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currentPoints.append(point)
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td3 = time.time()
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@ -434,7 +455,7 @@ def captureFacesPoints(i):
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# TODO only draw nose line now, so we can change color depending whether on screen or not
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results = {'currentPoint': currentPoint, 'currentPoints': currentPoints}
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results = {'currentPoint': currentPoint, 'currentPoints': currentPoints, 'currentVectors': currentVectors}
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results['im'] = im if not args.hide_preview else None
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try:
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@ -445,11 +466,14 @@ def captureFacesPoints(i):
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def captureVideo():
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c = cv2.VideoCapture(args.camera)
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# if not ding this we only have jittery 10fps
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c.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
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# set camera resoltion
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# c.set(3, 1280)
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# c.set(4, 720)
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c.set(3, 960)
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c.set(4, 540)
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c.set(3, 1280)
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c.set(4, 720)
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# c.set(3, 960)
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# c.set(4, 540)
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logger.debug("Camera FPS: {}".format(c.get(5)))
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while True:
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@ -462,225 +486,280 @@ def captureVideo():
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logger.debug("Que sizes: image: {}, points: {} ".format(photoQueue.qsize(), pointsQueue.qsize()))
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processes = []
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for i in range(args.processes - 2):
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p = multiprocessing.Process(target=captureFacesPoints, args=(i,))
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if __name__ == '__main__':
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processes = []
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for i in range(args.processes - 2):
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p = multiprocessing.Process(target=captureFacesPoints, args=(i,))
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p.daemon = True
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p.start()
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processes.append(p)
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p = multiprocessing.Process(target=captureVideo, args=())
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p.daemon = True
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p.start()
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processes.append(p)
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p = multiprocessing.Process(target=captureVideo, args=())
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p.daemon = True
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p.start()
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processes.append(p)
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newMetrics = np.zeros((metricsSize[1], metricsSize[0]))
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lastRunTime = 0
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while True:
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result = None
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try:
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newMetrics = np.zeros((metricsSize[1], metricsSize[0]))
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lastRunTime = 0
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while True:
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result = None
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te1 = time.time()
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result = pointsQueue.get()
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te1b = time.time()
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im = result['im']
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currentPoint = result['currentPoint']
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currentPoints = result['currentPoints']
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except queue.Empty as e:
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logger.warn('Result queue empty')
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if result is not None:
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if not args.hide_preview:
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# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px)
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cv2.rectangle(im, (9, 9), (51, 51), (255,255,255), 1)
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cv2.rectangle(im, (59, 9), (101, 51), (255,255,255), 1)
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cv2.line(im, (10,10), (10,50), (200,200,200), 2)
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cv2.line(im, (60,10), (60,50), (200,200,200), 2)
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# screen is 16:10
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cv2.rectangle(im, (9, 59), (91, 111), (255,255,255), 1)
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if transform is None:
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try:
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result = pointsQueue.get()
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te1b = time.time()
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im = result['im']
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currentPoint = result['currentPoint']
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currentPoints = result['currentPoints']
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currentVectors = result['currentVectors']
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except queue.Empty as e:
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logger.warn('Result queue empty')
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tr1 = time.time()
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if result is not None:
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if not args.hide_preview:
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cv2.putText(im, "1", (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tl'] is not None else (0,0,255))
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cv2.putText(im, "2", (85,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tr'] is not None else (0,0,255))
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cv2.putText(im, "3", (10,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['bl'] is not None else (0,0,255))
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cv2.putText(im, "4", (85,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['br'] is not None else (0,0,255))
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tm1 = 0
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tm2 = 0
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tm3 = 0
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tm4 = 0
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else:
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for point in currentPoints:
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# check if within coordinates:
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# dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br'])
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# dot2 = np.dot(coordinates['bl'] - point, coordinates['tl'] - coordinates['br'])
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# pointIn3 = [point[0], point[1], 0]
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# targetPoint = np.dot(pointIn3, transformationMatrix)
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# logger.info("Looking at", pointIn3, np.dot( transformationMatrix, pointIn3))
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targetPoint = transform(point)
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logger.info("Looking at {} {}".format(point, targetPoint) )
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# cv2.circle(im, (int(targetPoint[0]), int(targetPoint[1])), 2, (0,255,0), -1)
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# from 1920x1080 to 80x50
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# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px)
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cv2.rectangle(im, (9, 9), (51, 51), (255,255,255), 1)
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cv2.rectangle(im, (59, 9), (101, 51), (255,255,255), 1)
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cv2.line(im, (10,10), (10,50), (200,200,200), 2)
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cv2.line(im, (60,10), (60,50), (200,200,200), 2)
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# screen is 16:10
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cv2.rectangle(im, (9, 59), (91, 111), (255,255,255), 1)
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if transform is None:
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if not args.hide_preview:
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miniTargetPoint = (int(targetPoint[0] / metricsSize[0] * 80 + 10), int(targetPoint[1] / metricsSize[1] * 50 + 60))
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cv2.circle(im, miniTargetPoint, 2, (0,255,0), -1)
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targetInt = (int(targetPoint[0]), int(targetPoint[1]))
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# check if point fits on screen:
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# 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]:
|
||||
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]))
|
||||
#TODO: make it one numpy array action:
|
||||
for sx in range(spotSize[0]):
|
||||
for sy in range(spotSize[1]):
|
||||
mx = targetInt[0] + sx - (spotSize[0]-1)/2
|
||||
my = targetInt[1] + sy - (spotSize[1]-1)/2
|
||||
|
||||
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
|
||||
# and add to all metrics collected
|
||||
tm3 = time.time()
|
||||
# metrics = metrics + gaussian_filter(newMetrics, sigma = 13)
|
||||
|
||||
tm4 = time.time()
|
||||
# logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
|
||||
|
||||
# Display webcam image with overlays
|
||||
te2 = time.time()
|
||||
if result is not None and 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)
|
||||
logger.debug("Waited took %fs", te1b-te1)
|
||||
|
||||
|
||||
|
||||
# blur smooth the heatmap
|
||||
# logger.debug("Max blurred metrics: %f", np.max(metrics))
|
||||
|
||||
# update the heatmap output
|
||||
tm21 = time.time()
|
||||
t = tm21
|
||||
|
||||
|
||||
diffT = min(1, t - lastRunTime)
|
||||
lastRunTime = t
|
||||
# animDuration = 1
|
||||
# factor = animDuration
|
||||
|
||||
metrics = metrics + newMetrics*diffT
|
||||
newMetrics *= (1-diffT)
|
||||
print('MAXES', np.max(metrics), np.max(newMetrics), diffT, t - lastRunTime)
|
||||
|
||||
# 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
|
||||
normalisedMetricsColored = np.uint8(cm.nipy_spectral(normalisedMetrics)*255)
|
||||
normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_RGB2BGR)
|
||||
|
||||
tm22 = time.time()
|
||||
logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics))
|
||||
# logger.info(normalisedMetrics)
|
||||
tm23 = time.time()
|
||||
|
||||
cv2.imshow("test",normalisedMetricsColoredBGR)
|
||||
# image = Image.fromarray(normalisedMetricsColored)
|
||||
# wpercent = (imageWindowSize[0] / float(image.size[0]))
|
||||
# hsize = int((float(image.size[1]) * float(wpercent)))
|
||||
# renderImage = image.resize((renderSize[0], renderSize[1]))
|
||||
# print(renderImage.size, "lala")
|
||||
|
||||
# if args.queue_length:
|
||||
# imageQueue.append(image)
|
||||
# if len(imageQueue) > args.queue_length:
|
||||
# logger.warn("Use image from queue :-)")
|
||||
# image = imageQueue.pop(0)
|
||||
|
||||
# tkpi = ImageTk.PhotoImage(renderImage)
|
||||
# imageCanvas.delete("IMG")
|
||||
# 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("Total matrix time is %fs", tm4 - tm3 + tm2 - tm1 + tm24 - tm21)
|
||||
|
||||
if not args.hide_graph:
|
||||
te4 = time.time()
|
||||
axes.clear()
|
||||
if(len(dataframe) > 2):
|
||||
g = sns.kdeplot(dataframe['x'], dataframe['y'],ax=axes, n_levels=30, shade=True, cmap=cm.rainbow)
|
||||
canvas.draw()
|
||||
windowRoot.update()
|
||||
te5 = time.time()
|
||||
logger.debug("Drew graph & updated window in %fs", te5-te4)
|
||||
|
||||
if args.output_dir:
|
||||
# save output to dir
|
||||
now = tm24 # time.time()
|
||||
if now - lastSaveTime > args.save_interval:
|
||||
filename = os.path.join(
|
||||
args.output_dir,
|
||||
"frame{}.png".format(
|
||||
datetime.datetime.now().replace(microsecond=0).isoformat()
|
||||
)
|
||||
cv2.putText(im, "1", (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tl'] is not None else (0,0,255))
|
||||
cv2.putText(im, "2", (85,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tr'] is not None else (0,0,255))
|
||||
cv2.putText(im, "3", (10,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['bl'] is not None else (0,0,255))
|
||||
cv2.putText(im, "4", (85,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['br'] is not None else (0,0,255))
|
||||
tm1 = 0
|
||||
tm2 = 0
|
||||
tm3 = 0
|
||||
tm4 = 0
|
||||
else:
|
||||
for point in currentPoints:
|
||||
# check if within coordinates:
|
||||
# dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br'])
|
||||
# dot2 = np.dot(coordinates['bl'] - point, coordinates['tl'] - coordinates['br'])
|
||||
# pointIn3 = [point[0], point[1], 0]
|
||||
# targetPoint = np.dot(pointIn3, transformationMatrix)
|
||||
# logger.info("Looking at", pointIn3, np.dot( transformationMatrix, pointIn3))
|
||||
targetPoint = transform(point)
|
||||
logger.info("Looking at {} {}".format(point, targetPoint) )
|
||||
# cv2.circle(im, (int(targetPoint[0]), int(targetPoint[1])), 2, (0,255,0), -1)
|
||||
# from 1920x1080 to 80x50
|
||||
if not args.hide_preview:
|
||||
miniTargetPoint = (int(targetPoint[0] / metricsSize[0] * 80 + 10), int(targetPoint[1] / metricsSize[1] * 50 + 60))
|
||||
cv2.circle(im, miniTargetPoint, 2, (0,255,0), -1)
|
||||
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[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 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]):
|
||||
mx = targetInt[0] + sx - (spotSize[0]-1)/2
|
||||
my = targetInt[1] + sy - (spotSize[1]-1)/2
|
||||
|
||||
if mx >= 0 and my >= 0 and mx < metricsSize[0] and my < metricsSize[1]:
|
||||
newMetrics[my,mx] += spot[sx,sy] #/ 20
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
tm4 = time.time()
|
||||
# logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
|
||||
|
||||
# Display webcam image with overlays
|
||||
te2 = time.time()
|
||||
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))
|
||||
|
||||
# update the heatmap output
|
||||
tm21 = time.time()
|
||||
t = tm21
|
||||
|
||||
diffT = min(1, t - lastRunTime)
|
||||
lastRunTime = t
|
||||
# animDuration = 1
|
||||
# factor = animDuration
|
||||
|
||||
metrics = metrics + newMetrics*diffT
|
||||
newMetrics *= (1-diffT)
|
||||
print('MAXES', np.max(metrics), np.max(newMetrics), diffT, t - lastRunTime)
|
||||
|
||||
# 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.imwrite(filename, normalisedMetricsColoredBGR)
|
||||
# image.save(filename)
|
||||
|
||||
with open(lastMetricsFilename, 'wb') as fp:
|
||||
pickle.dump( metrics, fp )
|
||||
|
||||
logger.debug("Saved frame to {}".format(filename))
|
||||
lastSaveTime = now
|
||||
|
||||
# (optionally) very slowly fade out previous metrics:
|
||||
metrics = metrics * .9997
|
||||
|
||||
keyPress = cv2.waitKey(5)
|
||||
|
||||
if keyPress==27:
|
||||
break
|
||||
elif keyPress == ord('d'):
|
||||
logger.setLevel(logging.DEBUG)
|
||||
elif keyPress > -1 and currentPoint is not None:
|
||||
recalculate = False
|
||||
if keyPress == ord('1'):
|
||||
coordinates['tl'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 1')
|
||||
elif keyPress == ord('2'):
|
||||
coordinates['tr'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 2')
|
||||
elif keyPress == ord('3'):
|
||||
coordinates['bl'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 3')
|
||||
elif keyPress == ord('4'):
|
||||
coordinates['br'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 4')
|
||||
elif keyPress == ord('t') and transform is not None:
|
||||
logger.info("Coordinates {}".format(coordinates) )
|
||||
logger.info("Drawing area {}".format(screenDrawCorners))
|
||||
logger.info("Test point {}".format(currentPoint ))
|
||||
logger.info("Transformed point {}".format(transform(currentPoint)))
|
||||
|
||||
if recalculate is True and not any (x is None for x in coordinates.values()):
|
||||
logger.debug(coordinates.values())
|
||||
pickle.dump( coordinates, open( "coordinates.p", "wb" ) )
|
||||
logger.info("Saved coordinates")
|
||||
|
||||
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners)
|
||||
|
||||
duration = time.time()-te1
|
||||
fps = 1/duration
|
||||
logger.info("Rendering loop %fs %ffps", duration, fps)
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
# 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()
|
||||
|
||||
# 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)))
|
||||
# renderImage = image.resize((renderSize[0], renderSize[1]))
|
||||
# print(renderImage.size, "lala")
|
||||
|
||||
# if args.queue_length:
|
||||
# imageQueue.append(image)
|
||||
# if len(imageQueue) > args.queue_length:
|
||||
# logger.warn("Use image from queue :-)")
|
||||
# image = imageQueue.pop(0)
|
||||
|
||||
# tkpi = ImageTk.PhotoImage(renderImage)
|
||||
# imageCanvas.delete("IMG")
|
||||
# imagesprite = imageCanvas.create_image(renderSize[0]/2, renderSize[1]/2,image=tkpi, tags="IMG")
|
||||
# imageWindowRoot.update()
|
||||
tm24 = time.time()
|
||||
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:
|
||||
te4 = time.time()
|
||||
axes.clear()
|
||||
if(len(dataframe) > 2):
|
||||
g = sns.kdeplot(dataframe['x'], dataframe['y'],ax=axes, n_levels=30, shade=True, cmap=cm.rainbow)
|
||||
canvas.draw()
|
||||
windowRoot.update()
|
||||
te5 = time.time()
|
||||
logger.debug("Drew graph & updated window in %fs", te5-te4)
|
||||
|
||||
if args.output_dir:
|
||||
# save output to dir
|
||||
now = tm24 # time.time()
|
||||
if now - lastSaveTime > args.save_interval:
|
||||
filename = os.path.join(
|
||||
args.output_dir,
|
||||
"frame{}.png".format(
|
||||
datetime.datetime.now().replace(microsecond=0).isoformat()
|
||||
)
|
||||
)
|
||||
cv2.imwrite(filename, normalisedMetricsColoredBGR)
|
||||
# image.save(filename)
|
||||
|
||||
with open(lastMetricsFilename, 'wb') as fp:
|
||||
pickle.dump( metrics, fp )
|
||||
|
||||
logger.debug("Saved frame to {}".format(filename))
|
||||
lastSaveTime = now
|
||||
|
||||
# (optionally) very slowly fade out previous metrics:
|
||||
metrics = metrics * .9997
|
||||
|
||||
keyPress = cv2.waitKey(5)
|
||||
|
||||
if keyPress==27:
|
||||
break
|
||||
elif keyPress == ord('d'):
|
||||
logger.setLevel(logging.DEBUG)
|
||||
elif keyPress > -1 and currentPoint is not None:
|
||||
recalculate = False
|
||||
if keyPress == ord('1'):
|
||||
coordinates['tl'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 1')
|
||||
elif keyPress == ord('2'):
|
||||
coordinates['tr'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 2')
|
||||
elif keyPress == ord('3'):
|
||||
coordinates['bl'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 3')
|
||||
elif keyPress == ord('4'):
|
||||
coordinates['br'] = currentPoint
|
||||
recalculate = True
|
||||
logger.warn('Calibrate 4')
|
||||
elif keyPress == ord('t') and transform is not None:
|
||||
logger.info("Coordinates {}".format(coordinates) )
|
||||
logger.info("Drawing area {}".format(screenDrawCorners))
|
||||
logger.info("Test point {}".format(currentPoint ))
|
||||
logger.info("Transformed point {}".format(transform(currentPoint)))
|
||||
|
||||
if recalculate is True and not any (x is None for x in coordinates.values()):
|
||||
logger.debug(coordinates.values())
|
||||
pickle.dump( coordinates, open( "coordinates.p", "wb" ) )
|
||||
logger.info("Saved coordinates")
|
||||
|
||||
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners, True)
|
||||
|
||||
duration = time.time()-te1
|
||||
fps = 1/duration
|
||||
logger.info("Rendering loop %fs %ffps", duration, fps)
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
|
|
|
|||
|
|
@ -1,3 +0,0 @@
|
|||
*
|
||||
!.gitignore
|
||||
|
||||
Loading…
Reference in New Issue