boop
Co-authored-by: pejour <pejour@users.noreply.github.com> Co-authored-by: Laureηt <laurent@fainsin.bzh>
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87
src/main.py
87
src/main.py
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@ -3,14 +3,16 @@ import numpy as np
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from itertools import product
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import pickle
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import matplotlib.pyplot as plt
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import mcubes
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from rich.progress import track
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from borders import update_border
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from fvi import fast_voxel_intersect
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VOXEL_SIZE = 1e-2
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X_MIN, X_MAX = -1.3, 1.3
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Y_MIN, Y_MAX = -1.3, 1.3
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Z_MIN, Z_MAX = -0.3, 0.3
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VOXEL_SIZE = 1e-1
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X_MIN, X_MAX = -2, 2
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Y_MIN, Y_MAX = -2, 2
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Z_MIN, Z_MAX = -1, 1
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nb_frame = 24
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@ -19,15 +21,22 @@ points = np.array([[x, y, z, 1.0] for x, y, z in product(
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np.arange(Y_MIN, Y_MAX, VOXEL_SIZE),
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np.arange(Z_MIN, Z_MAX, VOXEL_SIZE))])
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mask = np.array([255, 255, 255])
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mask = 255 * np.ones((3,))
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test_point = [0, 0, 0, 1.0]
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positions = []
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proj_mats = []
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frames = []
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for k in range(nb_frame):
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frame = cv2.imread(f'/tmp/masks/Image{k:04}.png')
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frame = cv2.imread(f'data/torus/masks/Image{k:04}.png')
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frames.append(cv2.imread(f'data/torus/images/Image{k:04}.png'))
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with open(f"/tmp/cameras/{k:04d}.pickle", 'rb') as file:
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proj_mat = pickle.load(file)["P"]
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with open(f"data/torus/cameras/{k:04d}.pickle", 'rb') as file:
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matrices = pickle.load(file)
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proj_mat = matrices["P"]
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proj_mats.append(proj_mat)
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position = matrices["RT"][:, 3]
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positions.append(position)
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cam_points = proj_mat @ points.T
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cam_points /= cam_points[2,:]
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@ -42,8 +51,8 @@ for k in range(nb_frame):
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points = points[solid,:]
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# for cam_point in cam_points.T:
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# cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255*is_in[k], 0, 255*(not is_in[k])))
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# cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255, 0, 255))
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# cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255*is_in[k], 0, 255*(not is_in[k])))
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# cv2.circle(frame, (cam_point[0], cam_point[1]), 2, (255, 0, 255))
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# for k in range(nb_frame):
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# frame = cv2.imread(f'/tmp/masks/Image{k:04}.png')
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@ -69,9 +78,51 @@ voxel[idx[:,0], idx[:,1], idx[:,2]] = 1
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border = update_border(voxel)
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# 3D plot the result
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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ax.scatter(np.where(border)[0], np.where(border)[1], np.where(border)[2], c='b', marker='o', s=1)
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plt.axis('equal')
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plt.show()
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# # 3D plot the result
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# fig = plt.figure()
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# ax = fig.add_subplot(111, projection='3d')
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# ax.scatter(np.where(border)[0], np.where(border)[1], np.where(border)[2], c='b', marker='o', s=1)
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# plt.axis('equal')
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# plt.show()
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origin = np.array([X_MIN, Y_MIN, Z_MIN])
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step = np.array([VOXEL_SIZE, VOXEL_SIZE, VOXEL_SIZE])
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shape = np.array([int((X_MAX-X_MIN)/VOXEL_SIZE), int((Y_MAX-Y_MIN)/VOXEL_SIZE), int((Z_MAX-Z_MIN)/VOXEL_SIZE)])
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for idx in track(np.argwhere(border)):
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# coordonnées du centre du voxel
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start = np.array([
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X_MIN + (idx[0] + 0.5) * VOXEL_SIZE,
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Y_MIN + (idx[1] + 0.5) * VOXEL_SIZE,
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Z_MIN + (idx[2] + 0.5) * VOXEL_SIZE])
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# array qui contiendra les nuances de gris des frames qui voient le voxel
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values = []
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# pour chaque camera (frame)
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for i in range(nb_frame):
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# coordonnées du centre de la caméra
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end = positions[i]
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# si le rayon ne traverse aucun autre voxel entre le centre du voxel (idx) et le centre de la caméra
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if not fast_voxel_intersect(start, end, origin, step, shape):
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proj = proj_mats[i] @ np.array([start[0], start[1], start[2], 1.0])
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proj /= proj[2]
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proj = np.round(proj).astype(np.int32)
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values.append(frames[i][proj[1], proj[0]])
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# calcule écartype des valeurs
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std = np.std(values)
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# changer le levelset en fonction de l'écartype
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if std < 2:
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voxel[idx[0], idx[1], idx[2]] = 1
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else:
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voxel[idx[0], idx[1], idx[2]] = 0
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vertices, triangles = mcubes.marching_cubes(border, 0)
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mcubes.export_obj(vertices, triangles, "result.obj")
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