boop

Co-authored-by: pejour <pejour@users.noreply.github.com>
Co-authored-by: Laureηt <laurent@fainsin.bzh>

src/main.py

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