diff --git a/cuby.blend b/cuby.blend
index b7274c3..410d68a 100644
Binary files a/cuby.blend and b/cuby.blend differ
diff --git a/docs/figs/fvi.png b/docs/figs/fvi.png
new file mode 100644
index 0000000..1a503fe
Binary files /dev/null and b/docs/figs/fvi.png differ
diff --git a/docs/figs/lvl7_2D.gif b/docs/figs/lvl7_2D.gif
new file mode 100644
index 0000000..4e739d9
Binary files /dev/null and b/docs/figs/lvl7_2D.gif differ
diff --git a/docs/figs/lvl7_3D.gif b/docs/figs/lvl7_3D.gif
new file mode 100755
index 0000000..34f4cfe
Binary files /dev/null and b/docs/figs/lvl7_3D.gif differ
diff --git a/docs/slides.md b/docs/slides.md
index cf5f4b0..df2c6c0 100644
--- a/docs/slides.md
+++ b/docs/slides.md
@@ -2,8 +2,7 @@
theme: academic
class: text-white
coverAuthor: Laurent Fainsin, Damien Guillotin, Pierre-Eliot Jourdan
-coverBackgroundUrl: >-
- https://images.unsplash.com/photo-1655720408861-8b04c0724fd9?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8
+coverBackgroundUrl: https://images.unsplash.com/photo-1655720408861-8b04c0724fd9?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8
coverBackgroundSource: unplash
coverBackgroundSourceUrl: https://unsplash.com/photos/Vc0CmuIfMg0
coverDate: '2023-01-05'
@@ -27,6 +26,12 @@ Sujet 6 - Reformulation du MVS par level sets
---
+## Level sets 2D animé
+
+
+
+---
+
## Exemple Level sets 2D
@@ -36,3 +41,39 @@ Sujet 6 - Reformulation du MVS par level sets
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem - Olivier Faugeras, Renaud Keriven, 1998
+
+----
+
+## Exemple Level sets 3D
+
+
+
+---
+
+## Approximation du volume
+
+$\mu_t = \{ Q \in \mathbb{R}^3, u(Q) = t \}, t\in [0,1]\ \text{ou}\ t \in \{0, 1\}$
+
+---
+
+## Raytracing
+
+
+
+---
+
+## Raytracing
+
+
+
+---
+
+## Raytracing
+
+
+
+---
+
+## Raytracing
+
+
diff --git a/src/levelset.py b/src/levelset.py
index 3345ef2..3e31567 100644
--- a/src/levelset.py
+++ b/src/levelset.py
@@ -1,9 +1,9 @@
+import imageio.v2 as imageio
+import matplotlib.pyplot as plt
import mcubes
import numpy as np
-import matplotlib.pyplot as plt
-import imageio.v2 as imageio
import perlin_noise
-
+from rich.progress import track
# X, Y, Z = np.mgrid[:100, :100, :100]
# V = np.sqrt((X - 50)**2 + (Y - 50)**2 + (Z - 50)**2)
@@ -15,25 +15,29 @@ import perlin_noise
noise = perlin_noise.PerlinNoise(octaves=6, seed=1)
X, Y = np.mgrid[:100, :100]
-V = [[10 * noise([x/100, y/100]) + np.sqrt((x-50)**2 + (y-50)**2) for y in range(100)] for x in range(100)]
+V = [[10 * noise([x / 100, y / 100]) + np.sqrt((x - 50) ** 2 + (y - 50) ** 2) for y in range(100)] for x in range(100)]
V = np.array(V)
V = (V - V.min()) / (V.max() - V.min())
frame_list = []
-for i in np.linspace(0.05, 0.55, 100):
+for i in track(np.linspace(0.05, 0.55, 100)):
plt.clf()
plt.subplot(1, 2, 1)
plt.imshow(V, cmap="gray")
plt.contour(V, [i], colors="r")
plt.plot([0, 0, 100, 100, 0], [0, 100, 100, 0, 0], "k-")
- plt.axis('off')
+ plt.xlim(0, 100)
+ plt.ylim(0, 100)
+ plt.axis("off")
plt.subplot(1, 2, 2)
plt.imshow(V > i, cmap="gray")
plt.plot([0, 0, 100, 100, 0], [0, 100, 100, 0, 0], "k-")
- plt.axis('off')
+ plt.xlim(0, 100)
+ plt.ylim(0, 100)
+ plt.axis("off")
- plt.savefig(f"/tmp/frame.png")
+ plt.savefig(f"/tmp/frame.png", dpi=300, bbox_inches="tight", pad_inches=0, transparent=True)
frame_list.append(imageio.imread(f"/tmp/frame.png"))
-imageio.mimsave('picture.gif', frame_list + frame_list[::-1], fps=60)
+imageio.mimsave("docs/figs/lvl7.gif", frame_list + frame_list[::-1], fps=60)
diff --git a/tmp.py b/tmp.py
new file mode 100644
index 0000000..d5e9420
--- /dev/null
+++ b/tmp.py
@@ -0,0 +1,1326 @@
+import re
+
+import matplotlib.pyplot as plt
+
+h1 = {
+ "loss": [],
+ "c_acc": [],
+ "top1": [],
+ "top3": [],
+ "top5": [],
+ "val_loss": [],
+ "val_top1": [],
+ "val_top3": [],
+ "val_top5": [],
+}
+h2 = {
+ "loss": [],
+ "c_acc": [],
+ "top1": [],
+ "top3": [],
+ "top5": [],
+ "val_loss": [],
+ "val_top1": [],
+ "val_top3": [],
+ "val_top5": [],
+}
+h3 = {
+ "loss": [],
+ "c_acc": [],
+ "top1": [],
+ "top3": [],
+ "top5": [],
+ "val_loss": [],
+ "val_top1": [],
+ "val_top3": [],
+ "val_top5": [],
+}
+
+d1 = {
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+
+with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_baseline.txt", "r") as file:
+ for line in file:
+ matches = re.finditer(r"(val_loss|loss): ([0-9.]+)", line)
+ for match in matches:
+ metric_name = match.group(1)
+ metric_value = float(match.group(2))
+ h1[metric_name].append(metric_value)
+
+# with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_semisup.txt", "r") as file:
+# for line in file:
+# matches = re.finditer(r"(val_top1|val_top3): ([0-9.]+)", line)
+# for match in matches:
+# metric_name = match.group(1)
+# metric_value = float(match.group(2))
+# h2[metric_name].append(metric_value)
+
+# with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_finetuning.txt", "r") as file:
+# for line in file:
+# matches = re.finditer(r"(val_top1|val_top3): ([0-9.]+)", line)
+# for match in matches:
+# metric_name = match.group(1)
+# metric_value = float(match.group(2))
+# h3[metric_name].append(metric_value)
+
+# plt.figure(figsize=(15, 10))
+# plt.plot(d1["val"]["top1"], label="baseline_val_top1")
+# # plt.plot(h1["val_top3"], color="green", label="baseline_top3")
+# plt.plot(d2["val"]["top1"], label="semisup_val_top1")
+# # plt.plot(h2["val_top3"], color="green", label="semisup_top3")
+# plt.plot(d3["val"]["top1"], label="pretrain_val_top1")
+# # plt.plot(h3["val_top3"], color="green", label="finetuning_top3")
+# plt.xlabel("Epochs")
+# plt.ylabel("Accuracy")
+# plt.legend()
+# plt.grid()
+# plt.savefig("comp_accuracy_sgan.png", dpi=300, transparent=True, bbox_inches="tight", pad_inches=0)
+
+plt.figure(figsize=(15, 10))
+plt.plot(h1["loss"], color="purple", label="loss")
+plt.plot(h1["val_loss"], color="purple", label="val_loss", linestyle="--")
+plt.ylabel("Loss")
+plt.xlabel("Epochs")
+plt.legend()
+plt.grid()
+plt.savefig("baseline_loss_simclr.png", dpi=300, transparent=True, bbox_inches="tight", pad_inches=0)