fix: async io error

main.py

@@ -10,6 +10,7 @@ import pandas as pd
 import geopandas as gpd
 from pyproj.transformer import Transformer
 import cv2
+import asyncio
 from matplotlib import patches as mpatches
 from matplotlib import gridspec
 
@@ -75,18 +76,31 @@ def split_dataframe(df, chunk_size = 100):
         chunks.append(df[i*chunk_size:(i+1)*chunk_size])
     return chunks
 
-async def fetch(location, num_images, filter_undistort, disable_cam_filter, map_length, mpp):
+downloader = get_fpv.MapillaryDownloader(os.getenv("MLY_TOKEN"))
+loop = asyncio.get_event_loop()
+
+def generate_error_plot(error_message):
+    fig, ax = plt.subplots()
+    ax.text(0.5, 0.5, error_message, fontsize=12, va='center', ha='center', wrap=True)
+    ax.axis('off')
+
+    fig_img_path = 'fpv_bev.png'
+    fig.savefig(fig_img_path)
+    fig_img = plt.imread(fig_img_path)
+    return fig_img
+
+def fetch(location, num_images, filter_undistort, disable_cam_filter, map_length, mpp):
     TOTAL_LOOKED_INTO_LIMIT = 10000
 
     ################ FPV
-    downloader = get_fpv.MapillaryDownloader(os.getenv("MLY_TOKEN"))
+    
     bbox = get_city_boundary(query=location)
     tiles = get_fpv.get_tiles_from_boundary(boundary_info=dict(bound_type="auto_bbox", bbox=bbox), zoom=14)
     np.random.shuffle(tiles)
     total_looked_into = 0
     dfs_meta = list()
     for tile in tiles:
-        image_points_response = await downloader.get_tiles_image_points([tile])
+        image_points_response = loop.run_until_complete(downloader.get_tiles_image_points([tile]))
         if image_points_response is None:
             continue
         try:
@@ -98,7 +112,7 @@ async def fetch(location, num_images, filter_undistort, disable_cam_filter, map_
 
             for df in df_split:
                 image_ids = df["id"]
-                image_infos, num_fail = await get_fpv.fetch_image_infos(image_ids, downloader, infos_dir)
+                image_infos, num_fail = loop.run_until_complete(get_fpv.fetch_image_infos(image_ids, downloader, infos_dir))
                 df_meta = get_fpv.geojson_feature_list_to_pandas(image_infos.values())
 
                 # Some standardization of the data
@@ -114,9 +128,8 @@ async def fetch(location, num_images, filter_undistort, disable_cam_filter, map_
                 if total_rows > num_images:
                     break
                 elif total_looked_into > TOTAL_LOOKED_INTO_LIMIT:
-                    yield (f"Went through {total_looked_into} images and could not find images satisfying the filters."
-                           "\nPlease rerun or run the data engine locally for bulk time consuming operations.", None, None)
-                    return
+                    return generate_error_plot(f"Went through {total_looked_into} images and could not find images satisfying the filters."
+                           "\nPlease rerun or run the data engine locally for bulk time consuming operations.")
             if total_rows > num_images:
                 break
         except:
@@ -153,7 +166,7 @@ async def fetch(location, num_images, filter_undistort, disable_cam_filter, map_
         metadata_fmt = "\n".join(desc)
         # yield metadata_fmt, None, None
         image_urls = list(df_meta.set_index("id")["thumb_2048_url"].items())
-        num_fail = await get_fpv.fetch_images_pixels(image_urls, downloader, raw_image_dir)
+        num_fail = loop.run_until_complete(get_fpv.fetch_images_pixels(image_urls, downloader, raw_image_dir))
         if num_fail > 0:
             logger.error(f"Failed to download {num_fail} images.")
         
@@ -310,7 +323,7 @@ async def fetch(location, num_images, filter_undistort, disable_cam_filter, map_
     fig_img = plt.imread(fig_img_path)
     
 
-    yield fig_img
+    return fig_img
 
 filter_pipeline = filters.FilterPipeline.load_from_yaml("MapItAnywhere/mia/fpv/filter_pipelines/mia.yaml")
 filter_pipeline.verbose=False