Created BoundingBoxes

Player_Tracking/afl_player_tracking_and_crowd_monitoring/BoundingBoxes

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+# -*- coding: utf-8 -*-
+"""boundingboxeswithevents.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qjF8riAPbImWdy3NhnVNWT4C_vacw1j-
+"""
+
+import cv2
+import json
+import numpy as np
+import pandas as pd
+
+kick = pd.read_csv('kick_synced_annotations.csv')
+kick = pd.DataFrame(kick)
+
+kick.head()
+
+#reading the event data and storing it as a pandas data frame
+event = pd.read_csv('mark_synced_annotations.csv')
+event = pd.DataFrame(event)
+
+
+#reading the tracking data
+with open('mark_1 1track.json', 'r') as f:
+    annotations = json.load(f)
+
+# reading the raw video
+video_path = 'mark_1.mp4'
+cap = cv2.VideoCapture(video_path)
+
+
+fps = int(cap.get(cv2.CAP_PROP_FPS))
+width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+# output video
+output_path = 'mark_1_event.mp4'
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+
+# initiating the frame index as 0
+frame_index = 0
+
+
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        break
+    # iterating through traking data for each frame
+    if annotations['tracking_results'][frame_index]['frame_number'] == frame_index + 1:
+      #iterating through each object in the tracking results
+        for box in annotations['tracking_results'][frame_index]['players']:
+            x1, y1, x2, y2 = box['bbox']['x1'], box['bbox']['y1'], box['bbox']['x2'], box['bbox']['y2']
+            # Rectangle around the player, co-ordinants of box (x1, y1), (x2, y2), Box colour (255, 255, 0) Box Thickness = 2
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 255, 0), 2)
+            # box for label below the player
+            cv2.rectangle(frame, (x1, y2), (x1 +115, y2 +50), (255, 255, 0), -1)
+            # label for player only displaying the player ID to prevent the frame being over populated with information
+            cv2.putText(frame, str(box['player_id']), (x1, y2 +45), cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 3)
+
+            # for each frame the event data is checked
+            valid_event_rows = event.dropna(subset=['x1', 'y1', 'x2', 'y2'])
+            for index, row in valid_event_rows.iterrows():
+             # if the data matches the current frame it is checked for if it is an error or not it is then given a green or red box based off this information
+              if row['frame_id'] == frame_index + 1:
+                if row['error'] == 'Yes':
+                  cv2.rectangle(frame, (int(row['x1']), int(row['y1'])), (int(row['x2']), int(row['y2'])), (0, 0, 255), 2)
+                  cv2.putText(frame, 'Error', (int(row['x1']), int(row['y1']) +45), cv2.FONT_HERSHEY_PLAIN, 4, (0, 0, 255), 3)
+                else:
+                  cv2.rectangle(frame, (int(row['x1']), int(row['y1'])), (int(row['x2']), int(row['y2'])), (0, 255, 0), 2)
+                  cv2.putText(frame, str(row['event_name']), (int(row['x1']), int(row['y1']) +45), cv2.FONT_HERSHEY_PLAIN, 4, (0, 255, 0), 3)
+                  cv2.putText(frame, 'player ID: '+ str(row['player_id']) + ' Event: ' + str(row['event_name']), (50, 150), cv2.FONT_HERSHEY_PLAIN, 4, (0, 255, 0), 3)
+
+
+    # writing to the new output video
+    out.write(frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+    frame_index += 1
+
+cap.release()
+out.release()
+cv2.destroyAllWindows()