temp

app.py

@@ -115,22 +115,35 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
             col_ = 255*np.array(colors.to_rgba(col_))[:3]
             img2rsz[m,n, :] = col_[::-1]   
     fin_img.append(img2rsz)
-    
 
-    fig = plt.figure()
-    grid = ImageGrid(fig, 111, nrows_ncols=(2, 1),  axes_pad=0.1)
-    for ax, img in zip(grid, fin_img):
-        ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-        ax.axis('scaled')
-        ax.axis('off')
-    plt.tight_layout()
+    fig1 = plt.figure()
+    fig1.imshow(cv2.cvtColor(img1rsz, cv2.COLOR_BGR2RGB))
+    ax1 = plt.gca()
+    ax1.axis('scaled')
+    ax1.axis('off')
+
+    plt.tight_layout()    
+    
+    fig2 = plt.figure()
+    fig2.imshow(cv2.cvtColor(img2rsz, cv2.COLOR_BGR2RGB))
+    ax2 = plt.gca()
+    ax2.axis('scaled')
+    ax2.axis('off')
+       
+    # fig = plt.figure()
+    # grid = ImageGrid(fig, 111, nrows_ncols=(2, 1),  axes_pad=0.1)
+    # for ax, img in zip(grid, fin_img):
+    #     ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    #     ax.axis('scaled')
+    #     ax.axis('off')
+    # plt.tight_layout()
     # fig.suptitle("Matching SFs", fontsize=16)
 
     # fig.canvas.draw()
     # # Now we can save it to a numpy array.
     # data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
     # data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    return fig
+    return fig1,fig2
 
 
 # GRADIO APP
@@ -153,7 +166,7 @@ iface = gr.Interface(
         gr.inputs.Image(shape=(1024, 1024), type="pil"),
         gr.inputs.Slider(minimum=1, maximum=7, step=1, default=3, label="Scale"),
         gr.inputs.Slider(minimum=1, maximum=255, step=25, default=100, label="Binarization Threshold")],
-    outputs="plot",
+    outputs=["plot", "plot"],
     # outputs=gr.outputs.Image(shape=(1024,2048), type="plot"),
     title=title,
     layout="horizontal",