requirements.txt and packages.txt added

gradio_demo.py

@@ -8,7 +8,6 @@ import cv2
 import numpy as np
 import tensorflow as tf
 
-
 from ai.detection import detect
 from laeo_per_frame.interaction_per_frame_uncertainty import LAEO_computation
 from utils.hpe import hpe, project_ypr_in2d
@@ -16,7 +15,6 @@ from utils.img_util import resize_preserving_ar, draw_detections, percentage_to_
     visualize_vector
 
 
-
 def load_image(camera, ):
     # Capture the video frame by frame
     try:
@@ -26,12 +24,10 @@ def load_image(camera, ):
         logging.Logger('Error reading frame')
         return False, None
 
+
 def demo_play(img, laeo=True, rgb=False):
     # webcam in use
 
-
-
-
     # gpus = tf.config.list_physical_devices('GPU')
 
     # img = np.array(frame)
@@ -51,7 +47,6 @@ def demo_play(img, laeo=True, rgb=False):
     det, kpt = percentage_to_pixel(img.shape, detections['detection_boxes'], detections['detection_scores'],
                                    detections['detection_keypoints'], detections['detection_keypoint_scores'])
 
-
     # center_xy, yaw, pitch, roll = head_pose_estimation(kpt, 'centernet', gaze_model=gaze_model)
 
     # _________ extract hpe and print to img
@@ -82,11 +77,10 @@ def demo_play(img, laeo=True, rgb=False):
     binarize_uncertainty = False
     if laeo:
         interaction_matrix = LAEO_computation(people_list, clipping_value=clip_uncertainty,
-                                          clip=binarize_uncertainty)
+                                              clip=binarize_uncertainty)
     else:
         interaction_matrix = np.zeros((len(people_list), len(people_list)))
     # coloured arrow print per person
-    # TODO coloured arrow print per person
 
     for index, person in enumerate(people_list):
         green = round((max(interaction_matrix[index, :])) * 255)
@@ -99,19 +93,18 @@ def demo_play(img, laeo=True, rgb=False):
     return img
 
 
-
 demo = gr.Interface(
-    fn= demo_play,
-    inputs = [gr.Image(source="webcam", streaming=True),
-        gr.Checkbox(value=True, label="LAEO", info="Compute and display LAEO"),
-        gr.Checkbox(value=True, label="rgb", info="Display output on W/B image"),
-        ],
-    outputs="image",
-    live=True
-)
-
-if __name__ == '__main__':
-        if not os.path.exists("data"):
+        fn=demo_play,
+        inputs=[gr.Image(source="webcam", streaming=True),
+                gr.Checkbox(value=True, label="LAEO", info="Compute and display LAEO"),
+                gr.Checkbox(value=True, label="rgb", info="Display output on W/B image"),
+                ],
+        outputs="image",
+        live=True
+        )
+
+if __name__=='__main__':
+    if not os.path.exists("data"):
         gdown.download_folder("https://drive.google.com/drive/folders/1nQ1Cb_tBEhWxy183t-mIcVH7AhAfa6NO?usp=drive_link",
                               use_cookies=False)
     gaze_model_path = 'data/head_pose_estimation'

packages.txt

@@ -0,0 +1 @@
+python3-opencv

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+opencv-python
+tensorflow