TensorFlow Object Counting API

The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with the super high accuracy and reliability!

QUICK DEMO

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Cumulative Counting Mode (TensorFlow implementation):

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Real-Time Counting Mode (TensorFlow implementation):

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Object Tracking Mode (TensorFlow implementation):

• Tracking module was built on top of this approach.

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Object Counting On Single Image (TensorFlow implementation):

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Object Counting based R-CNN (Keras and TensorFlow implementation):

Object Segmentation & Counting based Mask R-CNN (Keras and TensorFlow implementation):

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BONUS: Custom Object Counting Mode (TensorFlow implementation):

You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the theory of transfer learning and show how to apply it in useful projects, are given at below.

Sample Project#1: Smurf Counting

More info can be found in here!

Sample Project#2: Barilla-Spaghetti Counting

More info can be found in here!

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The development is on progress! The API will be updated soon, the more talented and light-weight API will be available in this repo!

• Detailed API documentation and sample jupyter notebooks that explain basic usages of API will be added!

You can find a sample project - case study that uses TensorFlow Object Counting API in this repo.

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USAGE

1.) Usage of "Cumulative Counting Mode"

1.1) For detecting, tracking and counting the pedestrians with disabled color prediction

Usage of "Cumulative Counting Mode" for the "pedestrian counting" case:

is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects
roi = 385 # roi line position
deviation = 1 # the constant that represents the object counting area

object_counting_api.cumulative_object_counting_x_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation) # counting all the objects

Result of the "pedestrian counting" case:

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Source code of "pedestrian counting case-study": pedestrian_counting.py

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1.2) For detecting, tracking and counting the vehicles with enabled color prediction

Usage of "Cumulative Counting Mode" for the "vehicle counting" case:

is_color_recognition_enabled = True # set it to true for enabling the color prediction for the detected objects
roi = 200 # roi line position
deviation = 3 # the constant that represents the object counting area

object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation) # counting all the objects

Result of the "vehicle counting" case:

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Source code of "vehicle counting case-study": vehicle_counting.py

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2.) Usage of "Real-Time Counting Mode"

2.1) For detecting, tracking and counting the targeted object/s with disabled color prediction

Usage of "the targeted object is bicycle":

is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects
targeted_objects = "bicycle" 

object_counting_api.targeted_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled, targeted_objects) # targeted objects counting

Result of "the targeted object is bicycle":

Usage of "the targeted object is person":

is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects
targeted_objects = "person"

object_counting_api.targeted_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled, targeted_objects) # targeted objects counting

Result of "the targeted object is person":

Usage of "detecting, counting and tracking all the objects":

is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects

object_counting_api.object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled) # counting all the objects

Result of "detecting, counting and tracking all the objects":

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Usage of "detecting, counting and tracking the multiple targeted objects":

targeted_objects = "person, bicycle" # (for counting targeted objects) change it with your targeted objects
is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects

object_counting_api.targeted_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled, targeted_objects) # targeted objects counting

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2.2) For detecting, tracking and counting "all the objects with disabled color prediction"

Usage of detecting, counting and tracking "all the objects with disabled color prediction":

is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects

object_counting_api.object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled) # counting all the objects

Result of detecting, counting and tracking "all the objects with disabled color prediction":

Usage of detecting, counting and tracking "all the objects with enabled color prediction":

is_color_recognition_enabled = True # set it to true for enabling the color prediction for the detected objects

object_counting_api.object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled) # counting all the objects

Result of detecting, counting and tracking "all the objects with enabled color prediction":

3.) Usage of "Object Tracking Mode"

Just run object_tracking.py

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For sample usages of "Real-Time Counting Mode": real_time_counting.py

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The minimum object detection threshold can be set in this line in terms of percentage. The default minimum object detecion threshold is 0.5!

General Capabilities of The TensorFlow Object Counting API

Here are some cool capabilities of TensorFlow Object Counting API:

• Detect just the targeted objects
• Detect all the objects
• Count just the targeted objects
• Count all the objects
• Predict color of the targeted objects
• Predict color of all the objects
• Predict speed of the targeted objects
• Predict speed of all the objects
• Print out the detection-counting result in a .csv file as an analysis report
• Save and store detected objects as new images under detected_object folder
• Select, download and use state of the art models that are trained by Google Brain Team
• Use your own trained models or a fine-tuned model to detect spesific object/s
• Save detection and counting results as a new video or show detection and counting results in real time
• Process images or videos depending on your requirements

Here are some cool architectural design features of TensorFlow Object Counting API:

• Lightweigth, runs in real-time
• Scalable and well-designed framework, easy usage
• Gets "Pythonic Approach