JSON

main_noweb.py

@@ -20,6 +20,7 @@ import json
 # Image manipulation
 import numpy as np
 import cv2
+import ffmpeg
 
 print(torch.__version__)
 # Use GPU if available
@@ -38,6 +39,12 @@ track_model = YOLO('yolov8n.pt')  # Load an official Detect model
 
 print("[INFO]: Downloaded models!")
 
+def check_fps(video):
+    cap = cv2.VideoCapture(video)
+    nframes = cap.get(cv2.CAP_PROP_FRAME_COUNT) 
+    fps = cap.get(cv2.CAP_PROP_FPS)   
+    return nframes, fps
+
 def check_extension(video):
     split_tup = os.path.splitext(video)
 
@@ -135,14 +142,13 @@ def pose3d(video):
 
 def pose2d(video, kpt_threshold):
     video = check_extension(video)
-    print(device)
 
     # Define new unique folder
     add_dir = str(uuid.uuid4())
     vis_out_dir = os.path.join("/".join(video.split("/")[:-1]), add_dir)
 
     os.makedirs(add_dir)
-
+    print(check_fps(video))
     result_generator = human(video, 
                             vis_out_dir = add_dir,
                             #return_vis=True,
@@ -159,10 +165,7 @@ def pose2d(video, kpt_threshold):
     out_file = glob.glob(os.path.join(add_dir, "*.mp4")) #+ glob.glob(os.path.join(vis_out_dir, "*.webm")) 
     kpoints = glob.glob(os.path.join(add_dir, "*.json"))
 
-    with open("".join(kpoints), 'r') as f:
-        data = json.load(f)
-
-    return "".join(out_file), data # "".join(kpoints)
+    return "".join(out_file), "".join(kpoints)
 
 
 def pose2dhand(video, kpt_threshold):
@@ -211,7 +214,7 @@ with block:
                     video_output2 = gr.PlayableVideo(height=512,  label = "Estimate human 3d poses", show_label=True)
                     video_output3 = gr.PlayableVideo(height=512,  label = "Estimate human hand poses", show_label=True)
                     video_output4 = gr.Video(height=512, label = "Detection and tracking", show_label=True, format="mp4")
-                jsonoutput = gr.Code()
+                jsonoutput = gr.JSON()
 
 
         with gr.Tab("General information"):