batch true and queue true again

main_noweb.py

@@ -103,6 +103,7 @@ def pose3d(video, kpt_threshold):
 
     os.makedirs(add_dir)
     print(check_fps(video))
+    #video = human3d.preprocess(video, batch_size=8)
     result_generator = human3d(video, 
                             vis_out_dir = add_dir,
                             radius = 5,
@@ -221,43 +222,42 @@ def UI():
                     gr.Code(
                         value="""
                         
-                        # Importing packages needed  
-                        import json
-                        import numpy as np
-                        
-                        # First we load the data
-                        with open(file_path, 'r') as json_file:
-                            data = json.load(json_file)
-
-                        # The we define a function for calculating angles    
-                        def calculate_angle(a, b, c):
-                            a = np.array(a) # First point
-                            b = np.array(b) # Middle point
-                            c = np.array(c) # End point
-                            
-                            radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
-                            angle = np.abs(radians*180.0/np.pi)
-                            
-                            if angle >180.0:
-                                angle = 360-angle
-                                
-                            return angle 
+# Importing packages needed  
+import json
+import numpy as np
+
+# First we load the data
+with open(file_path, 'r') as json_file:
+    data = json.load(json_file)
+
+# The we define a function for calculating angles    
+def calculate_angle(a, b, c):
+    a = np.array(a) # First point
+    b = np.array(b) # Middle point
+    c = np.array(c) # End point
+    
+    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
+    angle = np.abs(radians*180.0/np.pi)
+    
+    if angle >180.0:
+        angle = 360-angle
+        
+    return angle 
 
-                        # We select the first identified person in the first frame (zero index) as an example
-                        # To calculate the angle of the right elbow we take the point before and after and according to the indices that will be 6 (right shoulder) and 9 (right wrist)
-                        predictions = data['predictions'][0]  # Assuming batch_size is 1
 
-                        # COCO keypoint indices
-                        shoulder_index = 6
-                        elbow_index = 8
-                        wrist_index = 9
+# COCO keypoint indices
+shoulder_index = 6
+elbow_index = 8
+wrist_index = 9
 
-                        shoulder_point = data[0]['instances'][0]['keypoints'][shoulder_index]
-                        elbow_point = data[0]['instances'][0]['keypoints'][elbow_index]
-                        wrist_point = data[0]['instances'][0]['keypoints'][wrist_index]
+# We select the first identified person in the first frame (zero index) as an example
+# To calculate the angle of the right elbow we take the point before and after and according to the indices that will be 6 (right shoulder) and 9 (right wrist)
+shoulder_point = data[0]['instances'][0]['keypoints'][shoulder_index]
+elbow_point = data[0]['instances'][0]['keypoints'][elbow_index]
+wrist_point = data[0]['instances'][0]['keypoints'][wrist_index]
 
-                        angle = calculate_angle(shoulder_point, elbow_point, wrist_point)
-                        print("Angle is: ", angle)    
+angle = calculate_angle(shoulder_point, elbow_point, wrist_point)
+print("Angle is: ", angle)    
 
                         """,
                         language="python",
@@ -315,7 +315,9 @@ def UI():
             submit_pose3d_file.click(fn=pose3d, 
                                     inputs= [video_input, file_kpthr], 
                                     outputs = [video_output2, jsonoutput],
-                                    queue=False)
+                                    batch=True, 
+                                    max_batch_size=16,
+                                    queue=True)
             
             submit_hand_file.click(fn=pose2dhand, 
                                 inputs= [video_input, file_kpthr],