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In this issue, the editor will bring you about Python code how to achieve AI target detection technology. The article is rich in content and analyzed and described from a professional point of view. I hope you can get something after reading this article.

With only 10 lines of Python code, we can achieve target detection in computer vision.

Use 10 lines of Python code to achieve AI target detection technology!

Yes, with only 10 lines of code, we can achieve the target detection technology that is widely used in AI products.

After reading the code, let's interpret the origin and implementation principle of these 10 lines of Python code.

The implementation of the 10 lines of code shown at the beginning is to use ImageAI.

How to easily realize Target Detection with the help of ImageAI

To perform target detection using ImageAI, you only need the following 4 steps:

1. Install Python on your computer

two。 Install ImageAI and its environmental dependencies

3. Download the target detection module file

4. Run the sample code, which is the 10 lines we showed.

Let's explain it in detail step by step.

1) download and install Python 3 from the Python official website

Python.org/

2) install the following environment dependencies through pip

1.Tensorflow

Pip install tensorflow

2.Numpy

Pip install numpy

3.SciPy

Pip install scipy

4.OpenCV

Pip install opencv-python

5.Pillow

Pip install pillow

6.Matplotlib

Pip install matplotlib

7.H5py

Pip install h6py

8.Keras

Pip install keras

9.ImageAI

Pip install

3) download the RetinaNet model file for target detection through this link.

Now that we have all the dependencies installed, we are ready to write our first target detection code. Create a Python file, name it (such as FirstDetection.py), then write the following code to the file, and copy the RetinaNet model file and the image you want to detect to the folder containing the Python file.

FirstDetection.py

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

Then run the code and wait for the console to print the results. After the console prints out the results, you can open the folder where FirstDetection.py is located, and you will find new images saved in it. For example, the following two sample images, and two new images saved after performing target detection.

Before target detection:

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

After target detection:

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

We can see that the name and probability of the detected object are displayed on the image.

Interpret 10 lines of code

Let's explain how these 10 lines of code work.

From imageai.Detection import ObjectDetection

Import os

Execution_path = os.getcwd ()

In the above three lines of code, we import the ImageAI target detection class on the first line, the Python os class on the second line, and a variable on the third line to get the path to the folder where our Python file, RetinaNet model file, and image are located.

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

In the above five lines of code, we define our target detection class on the first line, set the model type of RetinaNet on the second line, set the model path to the path of the RetinaNet model on the third line, load the model into the target detection class on the fourth line, and then call the detection function on the fifth line and parse it in the input and output image paths.

For eachObject in detections:

Print (eachObject ["name"] + ":" + eachObject ["percentage_probability"])

In the above two lines of code, we iterate over all the results returned by the detector.detectObjectFromImage function in the first line, and then print out the detection results (name and probability) of each object on the image by the model in the second line.

ImageAI supports many powerful target detection customization functions, one of which is the ability to extract the image of each object detected on the image. Simply parse the additional parameter extract_detected_objects=True into a detectObjectsFromImage function, as shown below, and the target detection class creates a folder for the image object, extracts each image, saves them in the newly created folder, and returns an additional array containing the paths through each image.

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

Using the first image as an example, we can get a separate image of each object detected in the image:

Use 10 lines of Python code to achieve AI target detection technology! (Python is the best language)

ImageAI provides a number of features that can be used for customization and production deployment of various target detection tasks (not described in detail)

Admittedly, just looking at each of these 10 lines of code is not amazing, but also with the help of a lot of libraries, but only 10 lines of code allows us to easily achieve the previously troublesome target detection, we can still talk about the word "power".

The above is the Python code that Xiaobian shares with you how to achieve AI target detection technology. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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