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detection.py

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+#----------------------Dependencies----------------------------#
+from imageai.Detection import VideoObjectDetection
+import os
+from matplotlib import pyplot as plt
+
+execution_path = os.getcwd()
+#-------------------------Functions----------------------------------------------#
+"""
+def forFull(output_arrays, count_arrays, average_output_count):
+    print("Array for the outputs of each frame ", output_arrays)
+    print("Array for output count for unique objects in each frame : ", count_arrays)
+    print("Output average count for unique objects in the entire video: ", average_output_count)
+    print("------------END OF THE VIDEO --------------")
+"""
+
+def forSeconds(second_number, output_arrays, count_arrays, average_output_count):
+    print("SECOND : ", second_number)
+    print("Array for the outputs of each frame ", output_arrays)
+    print("Array for output count for unique objects in each frame : ", count_arrays)
+    print("Output average count for unique objects in the last second: ", average_output_count)
+    print("------------END OF A SECOND --------------")
+
+#-------------------------------------------------------------------------------#
+detector = VideoObjectDetection()
+detector.setModelTypeAsRetinaNet()
+detector.setModelPath(os.path.join(execution_path,"resnet50_coco_best_v2.0.1.h5"))
+detector.loadModel(detection_speed="faster")
+plt.show()
+
+custom_objects = detector.CustomObjects(person=True)
+
+#--------------------------------Features---------------------------------------#
+video_path = detector.detectCustomObjectsFromVideo(
+minimum_percentage_probability=20,
+custom_objects=custom_objects,
+per_second_function=forSeconds,
+display_percentage_probability=False,
+display_object_name=True,
+log_progress=True,
+frames_per_second=60,
+#--------------------Input-File--------------------------------------------#
+input_file_path=os.path.join(execution_path,"crowd.mp4"),
+#--------------------Output-File-----------------------------------------#
+output_file_path=os.path.join(execution_path, "crowd_detection"))
+#---------------------Finish------------------------------------------#
+print(video_path)