pregenerate samples

app.py

@@ -96,7 +96,7 @@ def demo(Dataset, Model):
     query_modes = ["d+t", "r+d", "vanilla"]
     plot_title = {"r+d": "Recognized: ", "d+t": "OCR Input: ", "vanilla": ""}
     text_outputs = []
-    img_outputs = []
+    # img_outputs = []
     video_outputs = []
     for name in picked_samples:
         img_path = os.path.join(path, name)
@@ -118,15 +118,15 @@ def demo(Dataset, Model):
                 print("Cached video at:", video_filepath)
             video_outputs.append("./" + str(video_filepath))
 
-            fig, ax = plt.subplots()
-            ax.axis("off")
-            plot_ink(ink, ax, input_image=img)
-            buf = BytesIO()
-            fig.savefig(buf, format="png", bbox_inches="tight")
-            plt.close(fig)
-            buf.seek(0)
-            res = Image.open(buf)
-            img_outputs.append(res)
+            # fig, ax = plt.subplots()
+            # ax.axis("off")
+            # plot_ink(ink, ax, input_image=img)
+            # buf = BytesIO()
+            # fig.savefig(buf, format="png", bbox_inches="tight")
+            # plt.close(fig)
+            # buf.seek(0)
+            # res = Image.open(buf)
+            # img_outputs.append(res)
     return (
         img,
         text_outputs[0],