Let‘s Go,图像识别初探——ImageAI

我们常常能接触到图像识别，例如人脸识别，物体检测，自动驾驶等等。让我们通过ImageAI来初探图像识别吧。

开始

环境这里就不再赘述，请自觉查询官网
https://github.com/OlafenwaMoses/ImageAI

图像分类 Image Classification

目录结构

$ tree .
.
├── 1.jpg
├── FirstPrediction.py
├── image2.jpg
└── resnet50-19c8e357.pth

0 directories, 4 files

代码

from imageai.Classification import ImageClassification
import os

execution_path = os.getcwd()

prediction = ImageClassification()
prediction.setModelTypeAsResNet50()
prediction.setModelPath(os.path.join(execution_path, "resnet50-19c8e357.pth"))
prediction.loadModel()

# predictions, probabilities = prediction.classifyImage(os.path.join(execution_path, "1.jpg"), result_count=5 )
predictions, probabilities = prediction.classifyImage(os.path.join(execution_path, "image2.jpg"), result_count=5 )
for eachPrediction, eachProbability in zip(predictions, probabilities):
    print(eachPrediction , " : " , eachProbability)

输出

sports car  :  65.5919
convertible  :  12.3926
car wheel  :  8.0013
beach wagon  :  5.3135
grille  :  4.7835

物体检测 Object Detection

目录结构

$ tree
.
├── FirstObjectDetection.py
├── image2.jpg
├── image2new.jpg
└── yolov3.pt

0 directories, 4 files

代码

from imageai.Detection import ObjectDetection
import os

execution_path = os.getcwd()

detector = ObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath( os.path.join(execution_path, "yolov3.pt"))
detector.loadModel()
detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path, "image2.jpg"), output_image_path=os.path.join(execution_path, "image2new.jpg"), minimum_percentage_probability=30)

for eachObject in detections:
    print(eachObject["name"], " : ", eachObject["percentage_probability"], " : ", eachObject["box_points"])
    print("--------------------------------")

视频物体检测，跟踪，分析 Video Object Detection, Tracking and Analysis

目录结构

$ tree
.
├── FirstVideoObjectDetection.py
├── traffic.mp4
├── traffic_detected.mp4
└── yolov3.pt

0 directories, 4 files

代码

from imageai.Detection import VideoObjectDetection
import os

execution_path = os.getcwd()

detector = VideoObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath(os.path.join(execution_path, "yolov3.pt"))
detector.loadModel()

video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path, "traffic.mp4"),
                        output_file_path=os.path.join(execution_path, "traffic_detected")
                        , frames_per_second=20, log_progress=True)
print(video_path)