wip

app.py

@@ -13,6 +13,8 @@ def get_index_of_element_containing_word(lst, word):
     return indices[0] if indices else -1
 
 pred_global = None
+alpha_global = 0.5
+alpha_image = None
 
 stl_preds = np.load("stl_species.npy")
 df = pd.read_csv("gbif_full_filtered.csv")
@@ -36,30 +38,45 @@ def update_fn(val):
         return gr.Dropdown(label="Name", choices=obs, interactive=True)
 
 def text_fn(taxon, name):
-    global pred_global
+    global pred_global, alpha_global, alpha_image
 
     species_index = get_index_of_element_containing_word(obs, name)
     preds = np.flip(stl_preds[:, species_index].reshape(510, 510), 1)
 
     pred_global = preds
+    alpha_image = preds
     cmap = plt.get_cmap('plasma')
 
     rgba_img = cmap(preds)
     rgb_img = np.delete(rgba_img, 3, 2)
-    blend = Image.blend(stl_base, Image.fromarray((rgb_img * 255).astype(np.uint8)), 0.5)
+    blend = Image.blend(stl_base, Image.fromarray((rgb_img * 255).astype(np.uint8)), alpha_global)
     rgb_img = np.array(blend)
     #return gr.Image(preds, label="Predicted Heatmap", visible=True)
     return rgb_img
 
 def thresh_fn(val):
-    global pred_global
+    global pred_global, alpha_global, alpha_image
     preds = deepcopy(pred_global)
     preds[preds<val] = 0
     preds[preds>=val] = 1
+    alpha_image = deepcopy(preds)
     cmap = plt.get_cmap('plasma')
 
     rgba_img = cmap(preds)
     rgb_img = np.delete(rgba_img, 3, 2)
+    blend = Image.blend(stl_base, Image.fromarray((rgb_img * 255).astype(np.uint8)), alpha_global)
+    rgb_img = np.array(blend)
+    return rgb_img
+
+def alpha_fn(val):
+    global pred_global, alpha_global, alpha_image
+    alpha_global = val
+    preds = deepcopy(alpha_image)
+    cmap = plt.get_cmap('plasma')
+    rgba_img = cmap(preds)
+    rgb_img = np.delete(rgba_img, 3, 2)
+    blend = Image.blend(stl_base, Image.fromarray((rgb_img * 255).astype(np.uint8)), alpha_global)
+    rgb_img = np.array(blend)
     return rgb_img
 
 with gr.Blocks() as demo:
@@ -79,10 +96,14 @@ with gr.Blocks() as demo:
     with gr.Row():
         slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label="Confidence Threshold")
     
+    with gr.Row():
+        alpha = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label="Image Transparency")
+    
     with gr.Row():
         pred = gr.Image(label="Predicted Heatmap", visible=True)
     
     check_button.click(text_fn, inputs=[inp, out], outputs=[pred])
     slider.change(thresh_fn, slider, outputs=pred)
+    alpha.change(alpha_fn, alpha, outputs=pred)
     
 demo.launch()