feat: add cache results

app.py

@@ -7,9 +7,9 @@ from inference import inference
 
 input_video = gr.Video(mirror_webcam=False)
 
-dd_model = gr.Dropdown(choices=["YOLOv7", "YOLOv7 Tiny"], value="YOLOv7", label="Model")
+cb_cache_output = gr.Checkbox(value=True, label="Use chache example result")
 
-cb_motion_estimation = gr.Checkbox(value=True, label="Track camera movement")
+dd_model = gr.Dropdown(choices=["YOLOv7", "YOLOv7 Tiny"], value="YOLOv7", label="Model")
 
 features = gr.CheckboxGroup(
     choices=["Track camera movement", "Draw objects paths"],
@@ -28,6 +28,7 @@ slide_threshold = gr.Slider(minimum=0, maximum=1, value=0.25, label="Model confi
 
 intput_components = [
         input_video,
+        cb_cache_output,
         dd_model,
         features,
         dd_track_points,

demo_utils/files.py

@@ -0,0 +1,8 @@
+import os
+from typing import List
+
+
+def get_files(folder: str) -> List:
+    files_list = [file.split(".")[0] for file in os.listdir(folder)]
+
+    return files_list

inference.py

@@ -14,10 +14,12 @@ from demo_utils.configuration import (
 )
 from demo_utils.distance_function import euclidean_distance, iou
 from demo_utils.draw import center, draw
+from demo_utils.files import get_files
 
 
 def inference(
     input_video: str,
+    cb_cache_output: bool,
     model: str,
     features: str,
     track_points: str,
@@ -26,6 +28,14 @@ def inference(
     # temp_dir = tempfile.TemporaryDirectory()
     # output_path = temp_dir.name
 
+    if cb_cache_output:
+        for cache_video in get_files("examples"):
+            if input_video.find(cache_video) == -1:
+                continue
+
+            cache_video_name = f"outputs/{cache_video}_{model}_out.mp4"
+            return cache_video_name
+        
     coord_transformations = None
     paths_drawer = None
     fix_paths = False
@@ -65,7 +75,7 @@ def inference(
         paths_drawer = Paths(center, attenuation=0.01)
 
     if fix_paths:
-        paths_drawer = AbsolutePaths(max_history=5, thickness=2)
+        paths_drawer = AbsolutePaths(max_history=15, thickness=2)
 
     for frame in video:
         yolo_detections = model(

outputs/soccer_YOLOv7_out.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f913effd319ea0108016bab55dc4acf4275aafc5bd48b657a99948ba023f38fe
+size 7186625