j'ai push

This commit is contained in:
gdamms 2023-01-18 17:12:16 +01:00
parent c563fb2b60
commit 956129586d
3 changed files with 39 additions and 27 deletions

4
fvi.py
View file

@ -141,8 +141,8 @@ if __name__ == '__main__':
# Define segment # Define segment
# start = np.random.rand(2) * 20 - 10 # start = np.random.rand(2) * 20 - 10
# end = np.random.rand(2) * 20 - 10 # end = np.random.rand(2) * 20 - 10
start = np.array([0., 0.]) start = np.array([2.5, -3.5])
end = np.array([4., -4.]) end = np.array([7.0, 3.0])
# Plot # Plot
fig = plt.figure() fig = plt.figure()

60
main.py
View file

@ -7,50 +7,62 @@ from matrices_reader import *
PICKLE_PATH = "/tmp/pickle.truc" PICKLE_PATH = "/tmp/pickle.truc"
VOXEL_SIZE = 8e-2 VOXEL_SIZE = 2e-2
X_MIN, X_MAX = -2.0, 2.0 X_MIN, X_MAX = -2.0, 2.0
Y_MIN, Y_MAX = -2.0, 2.0 Y_MIN, Y_MAX = -2.0, 2.0
Z_MIN, Z_MAX = -2.0, 2.0 Z_MIN, Z_MAX = -2.0, 2.0
with open(PICKLE_PATH, 'rb') as file: with open(PICKLE_PATH, 'rb') as file:
projection_matrices = pickle.load(file) projection_matrices = pickle.load(file)
nb_frame = len(projection_matrices) nb_frame = len(projection_matrices) - 1
points = np.array([[x, y, z, 1.0] for x, y, z in product( points = np.array([[x, y, z, 1.0] for x, y, z in product(
np.arange(X_MIN, X_MAX, VOXEL_SIZE), np.arange(X_MIN, X_MAX, VOXEL_SIZE),
np.arange(Y_MIN, Y_MAX, VOXEL_SIZE), np.arange(Y_MIN, Y_MAX, VOXEL_SIZE),
np.arange(Z_MIN, Z_MAX, VOXEL_SIZE))]) np.arange(Z_MIN, Z_MAX, VOXEL_SIZE))])
background = np.array([255, 255, 255]) points_tor = np.array([[np.cos(theta), np.sin(theta), 0.0, 1.0] for theta, phi, r in product(
print('ok') np.arange(0, 2*np.pi, 0.1),
np.arange(0, 2*np.pi, 0.1),
np.arange(0.0, 0.25, 0.1))])
mask = np.array([255, 255, 255])
# is_in = []
# p = [0.0, 1.1, 0.0]
for k in range(nb_frame):
# points = np.array([p +[1.0]])
frame = cv2.imread(f'/tmp/masks/Image{k+1:04}.png')
proj_mat = projection_matrices[k]
cam_points = proj_mat @ points.T
cam_points /= cam_points[2,:]
cam_points = np.round(cam_points).astype(np.int32)
visible = np.logical_and.reduce((0 <= cam_points[0,:], cam_points[0,:] < frame.shape[1], 0 <= cam_points[1,:], cam_points[1,:] < frame.shape[0]))
cam_points = cam_points[:,visible]
points = points[visible,:]
solid = (frame[cam_points[1,:],cam_points[0,:]] == mask).all(axis=1)
cam_points = cam_points[:,solid]
points = points[solid,:]
# is_in.append(len(points) != 0)
# points = np.array([p + [1.0]])
for k in range(nb_frame): for k in range(nb_frame):
frame = cv2.imread(f'/tmp/masks/Image{k+1:04}.png') frame = cv2.imread(f'/tmp/masks/Image{k+1:04}.png')
proj_mat = projection_matrices[k] proj_mat = projection_matrices[k]
cam_points = proj_mat @ points.T cam_points = proj_mat @ points.T
cam_points /= cam_points[2,:] cam_points /= cam_points[2,:]
cam_points = np.round(cam_points, 0).astype(np.int32) cam_points = np.round(cam_points).astype(np.int32)
visible = np.logical_and.reduce((0 <= cam_points[0,:], cam_points[0,:] < frame.shape[1], 0 <= cam_points[1,:], cam_points[1,:] < frame.shape[0]))
cam_points = cam_points[:,visible]
points = points[visible,:]
solid = np.invert(((frame[cam_points[1,:],cam_points[0,:]] == background)).all(1))
cam_points = cam_points[:,solid]
points = points[solid,:]
print('ok')
for k in range(nb_frame):
frame = cv2.imread(f'/tmp/images/Image{k+1:04}.png')
proj_mat = projection_matrices[k]
cam_points = proj_mat @ points.T
cam_points /= cam_points[2,:]
cam_points = np.round(cam_points, 0).astype(np.int32)
for cam_point in cam_points.T: for cam_point in cam_points.T:
cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255, 0, 0)) # cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255*is_in[k], 0, 255*(not is_in[k])))
cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255, 0, 255))
cv2.imshow('Frame', frame) cv2.imshow('Frame', frame)
cv2.waitKey(0) cv2.waitKey(0)

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