Below is a sample function: FINAL NOTE ON VIDEO ANALYSIS : ImageAI allows you to obtain the detected video frame as a Numpy array at each frame, second and minute function. We defined a color index for the pie chart that we’ll use to visualize the average number of instances for each unique object detected in every second of our video. In the 4 lines above, we created a new instance of the VideoObjectDetection class in the first line, set the model type to RetinaNet in the second line, set the model path to the RetinaNet model file we downloaded and copied to the python file folder in the third line and load the model in the fourth line. With ImageAI you can run detection tasks and analyse images. See the results and link to download the videos below: Video Length = 1min 24seconds, Detection Speed = "normal" , Minimum Percentage Probability = 50 (default), Frame Detection Interval = 5, Detection Time = 15min 49seconds, >>> Download detected video at speed "normal" and interval=5, Video Length = 1min 24seconds, Detection Speed = "fast" , Minimum Percentage Probability = 40, Frame Detection Interval = 5, Detection Time = 5min 6seconds, >>> Download detected video at speed "fast" and interval=5, Video Length = 1min 24seconds, Detection Speed = "faster" , Minimum Percentage Probability = 30, Frame Detection Interval = 5, Detection Time = 3min 18seconds, >>> Download detected video at speed "faster" and interval=5, Video Length = 1min 24seconds, Detection Speed = "fastest" , Minimum Percentage Probability = 20 , Frame Detection Interval = 5, Detection Time = 2min 18seconds, Video Length = 1min 24seconds, Detection Speed = "flash" , Minimum Percentage Probability = 10, Frame Detection Interval = 5, Detection Time = 1min 27seconds, Download detected video at speed "flash" and interval=5. Find below examples of video analysis functions. .setModelTypeAsRetinaNet() , This function sets the model type of the object detection instance you created to the RetinaNet model, which means you will be performing your object detection tasks using the pre-trained “RetinaNet” model you downloaded from the links above. —parameter log_progress (optional) : Setting this parameter to True shows the progress of the video or live-feed as it is detected in the CLI. In the above code, after loading the model (can be done before loading the model as well), we defined a new variable See a sample code for this parameter below: © Copyright 2021, Moses Olafenwa and John Olafenwa This 1min 46sec video demonstrate the detection of a sample traffic video using ImageAI default VideoObjectDetection class. With ImageAI you can run detection tasks and analyse videos and live-video feeds from device cameras and IP cameras. Then, for every second of the video that is detected, the function will be parsed into the parameter will be executed and analytical data of the video will be parsed into the function. I started from this excellent Dat Tran article to explore the real-time object detection challenge, leading me to study python multiprocessing library to increase FPS with the Adrian Rosebrock’s website. Once this is set, the extra parameter you sepecified in your function will be the Numpy array of the detected frame. Zhuet al., 2017b]. Then the function returns a the path to the saved video which contains boxes and percentage probabilities rendered on objects detected in the video. Real Life Object Detection using OpenCV – Detecting objects in Live Video image processing. The default value is 20 but we recommend you set the value that suits your video or camera live-feed. to the custom objects variable we defined. the time of detection at a rate between 20% - 80%, and yet having just slight changes but accurate detection iii. Output Video An object detection model is trained to detect the presence and location of multiple classes of objects. Video Detection and Analysis. ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. 04/17/2019; 2 minutes to read; P; v; In this article. In this paper, we aim to present a unied method that can attack both the image and video detectors. The default values is True. To get started, download any of the pre-trained model that you want to use via the links below. If this parameter is set to a function, after every second of a video. object_detection.py Using OpenCV's VideoCapture() function, you can load live-video streams from a device camera, cameras connected by cable or IP cameras, and parse it into ImageAI's detectObjectsFromVideo() and detectCustomObjectsFromVideo() functions. Well-researched domains of object detection include face detection and pedestrian detection. To start performing video object detection, you must download the RetinaNet, YOLOv3 or TinyYOLOv3 object detection model via the links below: Because video object detection is a compute intensive tasks, we advise you perform this experiment using a computer with a NVIDIA GPU and the GPU version of Tensorflow installed. This parameter allows you to parse in a function you will want to execute after, each frame of the video is detected. The video object detection class provided only supports RetinaNet, YOLOv3 and TinyYOLOv3. The returned Numpy array will be parsed into the respective per_frame_function, per_second_function and per_minute_function (See details below). Find example code below: .setModelTypeAsTinyYOLOv3() , This function sets the model type of the object detection instance you created to the TinyYOLOv3 model, which means you will be performing your object detection tasks using the pre-trained “TinyYOLOv3” model you downloaded from the links above. the COCO dataset. And then, we adjust the mask to find purple and red objects. Let's take a look at the code below: Let us take a look at the part of the code that made this possible. For example, a model might be trained with images that contain various pieces of fruit, along with a label that specifies the class of fruit they represent (e.g. Is there any easy way to simply render the border at certain # of pixels for example? The default value is 50. – parameter display_percentage_probability (optional ) : This parameter can be used to hide the percentage probability of each object detected in the detected video if set to False. ImageAI allows you to obtain complete analysis of the entire video processed. The models supported are RetinaNet, YOLOv3 and TinyYOLOv3. ImageAI provided very powerful yet easy to use classes and functions to perform Video Object Detection and Tracking and Video analysis. See a sample funtion for this parameter below: —parameter video_complete_function (optional ) : This parameter allows you to parse in the name of a function you define. is detected, the function will be executed with the following values parsed into it: -- an array of dictionaries whose keys are position number of each frame present in the last second , and the value for each key is the array for each frame that contains the dictionaries for each object detected in the frame, -- an array of dictionaries, with each dictionary corresponding to each frame in the past second, and the keys of each dictionary are the name of the number of unique objects detected in each frame, and the key values are the number of instances of the objects found in the frame, -- a dictionary with its keys being the name of each unique object detected throughout the past second, and the key values are the average number of instances of the object found in all the frames contained in the past second, -- If return_detected_frame is set to True, the numpy array of the detected frame will be parsed as the fifth value into the function, "Array for output count for unique objects in each frame : ", "Output average count for unique objects in the last second: ", "------------END OF A SECOND --------------", "Output average count for unique objects in the last minute: ", "------------END OF A MINUTE --------------", "Output average count for unique objects in the entire video: ", "------------END OF THE VIDEO --------------", Video and Live-Feed Detection and Analysis, NOTE: ImageAI will switch to PyTorch backend starting from June, 2021, Custom Object Detection: Training and Inference. If you use more powerful NVIDIA GPUs, you will definitely have faster detection time than stated above. See a sample below: ImageAI now provides detection speeds for all video object detection tasks. ImageAI provides very powerful yet easy to use classes and functions to perform Image Object Detection and Extraction. The program starts with a default Hue range (90, 140) which can detect blue objects. How should I go about changing the border width for the video object detection? ImageAI provides very convenient and powerful methods to perform object detection in videos and track specific object (s). Find example code below: .detectObjectsFromVideo() , This is the function that performs object detecttion on a video file or video live-feed after the model has been loaded into the instance you created. It deals with identifying and tracking objects present in images and videos. ImageAI provides you the option to adjust the video frame detections which can speed up your video detection process. This VideoObjectDetection class provides you function to detect objects in videos and live-feed from device cameras and IP cameras, using pre-trained models that was trained on The data returned can be visualized or saved in a NoSQL database for future processing and visualization. All you need is to load the camera with OpenCV’s VideoCapture() function and parse the object into this parameter. When the detection starts on a video feed, be it from a video file or camera input, the result will have the format as below: For any function you parse into the per_frame_function, the function will be executed after every single video frame is processed and he following will be parsed into it: In the above result, the video was processed and saved in 10 frames per second (FPS). Video Length = 1min 24seconds, Detection Speed = "normal" , Minimum Percentage Probability = 50 (default), Detection Time = 29min 3seconds, Video Length = 1min 24seconds, Detection Speed = "fast" , Minimum Percentage Probability = 40, Detection Time = 11min 6seconds Find example code below: .setModelPath() , This function accepts a string which must be the path to the model file you downloaded and must corresponds to the model type you set for your object detection instance. Detect common objects in images. Then write the code below into the python file: Let us make a breakdown of the object detection code that we used above. The difference is that the index returned corresponds to the minute index, the output_arrays is an array that contains the number of FPS * 60 number of arrays (in the code example above, 10 frames per second(fps) * 60 seconds = 600 frames = 600 arrays), and the count_arrays is an array that contains the number of FPS * 60 number of dictionaries (in the code example above, 10 frames per second(fps) * 60 seconds = 600 frames = 600 dictionaries) and the average_output_count is a dictionary that covers all the objects detected in all the frames contained in the last minute. Once you have downloaded the model you chose to use, create an instance of the VideoObjectDetection as seen below: Once you have created an instance of the class, you can call the functions below to set its properties and detect objects in a video. It is set to True by default. To go further and in order to enhance portability, I wanted to integrate my project into a Docker container. Lowering the value shows more objects while increasing the value ensures objects with the highest accuracy are detected. Learn More. 2.2 Adversarial Attack for Object Detection. The video object detection class provided only supports RetinaNet, YOLOv3 and TinyYOLOv3. Training Data for Object Detection and Semantic Segmentation. This is to tell the model to detect only the object we set to True. Download RetinaNet Model - resnet50_coco_best_v2.1.0.h5, Download TinyYOLOv3 Model - yolo-tiny.h5. All you need is to define a function like the forSecond or forMinute function and set the video_complete_function parameter into your .detectObjectsFromVideo() or .detectCustomObjectsFromVideo() function. —parameter detection_timeout (optional) : This function allows you to state the number of seconds of a video that should be detected after which the detection function stop processing the video. 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