Last Updated on 2021-08-01 by Clay
It needs to be stated in advance that the teaching of this article is learning from: https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606. You can also go directly to that website to learn.
In addition, here is the developer's Github: https://github.com/OlafenwaMoses/ImageAI?source=post_page-----d6cb4d86f606---------------------- , and I have offered my star in admiration =)
Since this tool is really useful, I wrote this article to promote it.
Basically as advertised in the original text, as long as the basic related packages are installed, the code to call ImageAI for image recognition is almost less than 10 lines.
And the best thing about this kit is that its computing speed is very, very fast. In the past, I was impressed by the classic models such as Yolov3 and SSD in the object recognition part of image processing. Of course, these models must use the GPU, and only use the speed of the CPU. Oh my God, it is simply unimaginable.
However, ImageAI only uses CPU computing but it is fast and very fast. (Of course, it may be because we directly use their trained models, and there is another chapter in it later that tells us that if we want to retrain their models for object recognition, we still need to use GPU, and I seem to have seen Yolov3 XD)
So, the following begins to explain how I use this convenient package.
Install Dependencies
It should be noted in advance that the version of Tensorflow installed with 1.4 (including later) is better and does not need to be upgraded to 2.0, but the developer has mentioned that 2.0 supports will coming soon, which is worth looking forward to.
sudo pip3 install opencv-python
sudo pip3 install pillow
sudo pip3 install numpy
sudo pip3 install scipy
sudo pip3 install matplotlib
sudo pip3 install h5py
sudo pip3 install tensorflow==1.14
sudo pip3 install kerasFinally, of course, install the most important:
sudo pip3 install imageaiIf there is no way to run the ImageAI suite, please go to the developer's Github or the teaching article on Medium to confirm the version, I think it should be able to solve the problem smoothly.
In addition, we have to download the pre-trained model provided by the developer. Save the model where the program will run.
In order to test the effect of the model, I do not plan to use the image provided by the developer. I just found this image on Google.
I also saved this picture in the same folder as the code, named test.jpg
Program
# -*- coding: utf-8 -*- import os from imageai.Detection import ObjectDetection detector = ObjectDetection() detector.setModelTypeAsRetinaNet() detector.setModelPath('resnet50_coco_best_v2.0.1.h5') detector.loadModel() detections = detector.detectObjectsFromImage(input_image='test.jpg', output_image_path='test_new.jpg') for eachObject in detections: print('{}:{}'.format(eachObject['name'], eachObject['percentage_probability']))
Output:
car:54.42100763320923
car:61.10377311706543
person:58.36105942726135
motorcycle:54.96629476547241
motorcycle:50.44354200363159
person:57.037585973739624
bus:66.54394865036011
car:65.67930579185486
car:54.16930913925171
car:56.64970874786377
car:72.12932705879211
car:68.234783411026
car:64.10300731658936
car:58.10954570770264
car:63.097649812698364
car:53.60064506530762
car:64.27150964736938
car:66.74740314483643
car:58.199405670166016
car:58.271121978759766
person:63.879597187042236
person:59.48329567909241
car:76.3867735862732
car:80.26036024093628
car:84.88558530807495
car:81.44459128379822
car:86.51140332221985
person:54.11458611488342
car:96.40277624130249
person:50.599128007888794
motorcycle:54.92902398109436
person:66.21726155281067
car:92.68029928207397
car:95.23538947105408
car:67.5695538520813
car:89.36827182769775
car:97.36456871032715
car:99.09504652023315Before:
After:
the speed is very fast! The code is also quite streamlined. I strongly recommend this RetinaNet encapsulated Python Package to developers who have such needs.