fx

app.py

@@ -68,7 +68,7 @@ class AgeGenderModel(Wav2Vec2PreTrainedModel):
         logits_gender = torch.softmax(self.gender(hidden_states), dim=1)
 
         return hidden_states, logits_age, logits_gender
-    
+
 # AgeGenderModel.forward() is switched to accept computed frozen CNN7 features from ExpressioNmodel
 
 def _forward(
@@ -178,7 +178,7 @@ age_gender_model.wav2vec2.forward = types.MethodType(_forward, age_gender_model)
 expression_model.wav2vec2.forward = types.MethodType(_forward_and_cnn7, expression_model)
 
 def process_func(x: np.ndarray, sampling_rate: int) -> typing.Tuple[str, dict, str]:
-    
+
     # batch audio
     y = expression_processor(x, sampling_rate=sampling_rate)
     y = y['input_values'][0]
@@ -227,7 +227,7 @@ def recognize(input_file: str) -> typing.Tuple[str, dict, str]:
     return process_func(signal, target_rate)
 
 
-def plot_expression(arousal, dominance, valence):
+def plot_expression_RIGID(arousal, dominance, valence):
     r"""3D pixel plot of arousal, dominance, valence."""
     # Voxels per dimension
     voxels = 7
@@ -271,6 +271,105 @@ def plot_expression(arousal, dominance, valence):
         verticalalignment="top",
     )
 
+COLORMAP = plt.get_cmap('coolwarm')
+N_PIX = 5
+
+matplotlib.rcParams['mathtext.fontset'] = 'stix'
+matplotlib.rcParams['font.family'] = 'STIXGeneral'
+
+def explode(data):
+    '''replicate 16 x 16 x 16 cube to edges array 31 x 31 x 31'''
+    size = np.array(data.shape)*2
+    data_e = np.zeros(size - 1, dtype=data.dtype)
+    data_e[::2, ::2, ::2] = data
+    return data_e
+
+
+def plot_expression(arousal, dominance, valence):
+
+    '''_h = cuda tensor (N_PIX, N_PIX, N_PIX)'''
+
+    N_PIX=5
+    _h = np.random.rand(N_PIX, N_PIX, N_PIX) * 1e-3
+    adv = np.array([arousal, .994 - dominance, valence]).clip(0, .99)
+    arousal, dominance, valence = (adv * N_PIX).astype(np.int64)  # find voxel
+    _h[arousal, dominance, valence] = .22
+
+
+
+
+    filled = np.ones((N_PIX, N_PIX, N_PIX), dtype=bool)
+
+    # upscale the above voxel image, leaving gaps
+    filled_2 = explode(filled)
+
+    # Shrink the gaps
+    x, y, z = np.indices(np.array(filled_2.shape) + 1).astype(float) // 2
+    x[1::2, :, :] += 1
+    y[:, 1::2, :] += 1
+    z[:, :, 1::2] += 1
+
+    ax = plt.figure().add_subplot(projection='3d')
+
+    f_2 = np.ones([2 * N_PIX - 1,
+                   2 * N_PIX - 1,
+                   2 * N_PIX - 1, 4], dtype=np.float64)
+    f_2[:, :, :, 3] = explode(_h)
+    cm = plt.get_cmap('cool')
+    f_2[:, :, :, :3] = cm(f_2[:, :, :, 3])[..., :3]
+
+    f_2[:, :, :, 3] = f_2[:, :, :, 3].clip(.01, .74)
+
+    print(f_2.shape, 'f_2 AAAA')
+    ecolors_2 = f_2
+
+    ax.voxels(x, y, z, filled_2, facecolors=f_2, edgecolors=.006 * ecolors_2)
+    ax.set_aspect('equal')
+    ax.set_zticks([0, N_PIX])
+    ax.set_xticks([0, N_PIX])
+    ax.set_yticks([0, N_PIX])
+
+    ax.set_zticklabels([f'{n/N_PIX:.2f}'[0:] for n in ax.get_zticks()])
+    ax.set_zlabel('valence', fontsize=10, labelpad=0)
+    ax.set_xticklabels([f'{n/N_PIX:.2f}' for n in ax.get_xticks()])
+    ax.set_xlabel('arousal', fontsize=10, labelpad=7)
+    # The y-axis rotation is corrected here from 275 to 90 degrees
+    ax.set_yticklabels([f'{1-n/N_PIX:.2f}' for n in ax.get_yticks()], rotation=90)
+    ax.set_ylabel('dominance', fontsize=10, labelpad=10)
+    ax.grid(False)
+
+ 
+
+
+    ax.plot([N_PIX, N_PIX], [0, N_PIX + .2], [N_PIX, N_PIX], 'g', linewidth=1)
+    ax.plot([0, N_PIX], [N_PIX, N_PIX + .24], [N_PIX, N_PIX], 'k', linewidth=1)
+
+    # Bottom face lines
+    # ax.plot([0, N_PIX + line_extension], [0, 0], [0, 0], 'y', linewidth=1)
+    # ax.plot([0, 0], [0, N_PIX + line_extension], [0, 0], 'r', linewidth=1)
+    # ax.plot([N_PIX, N_PIX + line_extension], [0, N_PIX], [0, 0], 'm', linewidth=1)
+    # ax.plot([0, N_PIX], [N_PIX, N_PIX + line_extension], [0, 0], 'c', linewidth=1)
+
+    # Vertical lines
+    # ax.plot([0, 0], [0, 0], [0, N_PIX + line_extension], 'b', linewidth=1)
+    # ax.plot([N_PIX, N_PIX], [0, 0], [0, N_PIX + line_extension], 'w', linewidth=1)
+    # ax.plot([N_PIX, N_PIX], [N_PIX, N_PIX], [0, N_PIX + line_extension], 'orange', linewidth=1)
+    # ax.plot([0, 0], [N_PIX, N_PIX], [0, N_PIX + line_extension], 'lime', linewidth=1)
+    
+    # # Missing lines on the top face
+    ax.plot([0, 0], [0, N_PIX], [N_PIX, N_PIX], 'darkred', linewidth=1)
+    ax.plot([0, N_PIX], [0, 0], [N_PIX, N_PIX], 'darkblue', linewidth=1)
+
+    # Set pane colors after plotting the lines
+    ax.w_xaxis.set_pane_color((0.8, 0.8, 0.8, 0.5))
+    ax.w_yaxis.set_pane_color((0.8, 0.8, 0.8, 0.5))
+    ax.w_zaxis.set_pane_color((0.8, 0.8, 0.8, 0.0))
+
+    # Restore the limits to prevent the plot from expanding
+    ax.set_xlim(0, N_PIX)
+    ax.set_ylim(0, N_PIX)
+    ax.set_zlim(0, N_PIX)
+# ------
 
 
 description = (