def get_random_sample(smpl):
pose = 0.65 * (np.random.rand(*smpl.pose_shape) - 0.5)
beta = 0.06 * (np.random.rand(*smpl.beta_shape) - 0.5)
trans = np.zeros(smpl.trans_shape)
parameters = np.concatenate([pose.ravel(), beta, trans])
# Create the body mesh
pointcloud = smpl.set_params(beta=beta, pose=pose, trans=trans)
# Render the silhouette
mesh = Mesh(pointcloud=pointcloud)
mesh.faces = smpl.faces
silhouette = mesh.render_silhouette(dim=(256, 256), show=False)
return silhouette, mesh, parameters
def on_epoch_end(self, epoch, logs=None):
""" Store the model loss and accuracy at the end of every epoch, and store a model prediction on data """
self.epoch_log.write(
json.dumps({
'epoch': epoch,
'loss': logs['loss']
}) + '\n')
if (epoch + 1) % self.period == 0 or epoch == 0:
# Predict on all of the given silhouettes
for data_type, data in self.pred_data.items():
if data is not None:
if not isinstance(data, list) or type(data) == np.array:
data = np.array(data)
data = data.reshape(
(1, data.shape[0], data.shape[1], data.shape[2]))
#for i, silhouette in enumerate(data):
# # Save silhouettes
# silhouette *= 255
# cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.gt_silh_{:03d}.png".format(data_type, epoch + 1, i)), silhouette.astype("uint8"))
preds = self.model.predict(data)
#print("Predictions: " + str(preds))
for i, pred in enumerate(preds[1], 1):
#self.smpl.set_params(pred[:72].reshape((24, 3)), pred[72:82], pred[82:])
#self.smpl.save_to_obj(os.path.join(self.pred_path, "{}_pred_{:03d}.obj".format(data_type, i)))
#print_mesh(os.path.join(self.pred_path, "epoch.{:03d}.{}_gt_{:03d}.obj".format(epoch, data_type, i)), gt[i-1], smpl.faces)
print_mesh(
os.path.join(
self.pred_path,
"{}_epoch.{:03d}.pred_{:03d}.obj".format(
data_type, epoch + 1, i)), pred,
self.smpl.faces)
# Store predicted silhouette and the difference between it and the GT silhouette
gt_silhouette = (data[i - 1] *
255).astype("uint8").reshape(
data.shape[1], data.shape[2])
#cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.gt_silh_{:03d}.png".format(data_type, epoch + 1, i)), gt_silhouette)
pred_silhouette = Mesh(
pointcloud=pred).render_silhouette(show=False)
#cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.pred_silh_{:03d}.png".format(data_type, epoch + 1, i)), pred_silhouette)
diff_silh = abs(gt_silhouette - pred_silhouette)
#print(diff_silh.shape)
#cv2.imshow("Diff silh", diff_silh)
#cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.diff_silh_{:03d}.png".format(data_type, epoch + 1, i)), diff_silh.astype("uint8"))
silh_comp = np.concatenate(
[gt_silhouette, pred_silhouette, diff_silh])
cv2.imwrite(
os.path.join(
self.pred_path,
"{}_epoch.{:03d}.silh_comp_{:03d}.png".format(
data_type, epoch + 1, i)),
silh_comp.astype("uint8"))
if self.gt_pc[data_type] is not None:
print_mesh(
os.path.join(
self.pred_path,
"{}_epoch.{:03d}.gt_pc_{:03d}.obj".format(
data_type, epoch + 1, i)),
self.gt_pc[data_type], self.smpl.faces)
print_point_clouds(
os.path.join(
self.pred_path,
"{}_epoch.{:03d}.comparison_{:03d}.obj".
format(data_type, epoch + 1, i)),
[pred, self.gt_pc[data_type]], [(255, 0, 0),
(0, 255, 0)])
if self.visualise:
# Show a random sample
rand_index = np.random.randint(low=0,
high=len(data)) + 1
mesh = Mesh(filepath=os.path.join(
self.pred_path, "{}_epoch.{:03d}.pred_{:03d}.obj".
format(data_type, epoch + 1, rand_index)))
# Show the true silhouette
true_silh = data[rand_index - 1]
true_silh = true_silh.reshape(true_silh.shape[:-1])
plt.imshow(true_silh, cmap='gray')
plt.title("True {} silhouette {:03d}".format(
data_type, rand_index))
plt.show()
# Show the predicted silhouette and mesh
mesh.render_silhouette(
title="Predicted {} silhouette {:03d}".format(
data_type, rand_index))
diff_silh = cv2.imread(
"{}_epoch.{:03d}.diff_silh_{:03d}.png".format(
data_type, epoch + 1, rand_index))
cv2.imshow(
"Predicted {} silhouette {:03d}".format(
data_type, rand_index), diff_silh)
try:
mesh.render3D()
except Exception:
pass
dilate = 1
if dilate == 1:
morph_mask = np.array([[0.34, 0.34, 0.34], [0.34, 1.00, 0.34],
[0.34, 0.34, 0.34]])
new_img = binary_closing(shifted_img != 0,
structure=morph_mask,
iterations=1).astype(np.uint8)
new_img *= 255
else:
new_img = shifted_img
return new_img
if __name__ == "__main__":
mesh_dir = "/data/cvfs/hjhb2/projects/deep_optimiser/example_meshes/"
obj_paths = os.listdir(mesh_dir)
for obj_path in obj_paths:
mesh = Mesh(os.path.join(mesh_dir, obj_path))
silh = mesh.render_silhouette(dim=[256, 256], show=True)
normalised_silh = normalise_img(silh, dim=(128, 128))
#plt.imshow(silh_cropped, cmap="gray")
plt.imshow(normalised_silh, cmap="gray")
plt.show()
augmented_silh = augment_image(normalised_silh)
plt.imshow(augmented_silh, cmap="gray")
plt.show()
