Python基于ImageAI实现图像识别详解

from imageai.Prediction import ImagePrediction
import os
import time
#开始计时
start_time=time.time()
execution_path=os.getcwd()
#对ImagePrediction类进行实例化
prediction=ImagePrediction()
#设置算法模型类型
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path,'resent50_weights_tf_dim_ordering_tf_kernels.h5'))
prediction.loadModel()
predictions,probabilities=prediction.predictioImage(os.path.join(execution_path,'sample.jpg'),result_count=5)
end_time=time.time()
for eachPrediction,eachProbability in zip(predictions,probabilities):
    print(eachPrediction+":"+str(eachProbability))
print('Total time cost:',end_time-start_time)

#多图像预测
from image.Prediction import ImagePrediction
import os
execution_path=os.getcwd()
#初始化预测器
multiple_prediction=ImagePrediction()
multiple_prediction.setModelTypeAsResNet()
#设置模型文件路径
multiple_prediction.setModelPath(os.path.join(execution_path,'resent50_weights_tf_ordering_tf_kernels.h5'))
#加载模型
multiple_prediction.loadModel()
all_images_array=[]
all_files=os.listdir(execution_path)
for each_file in all_files:
    if(each_file.endswith('.jpg') or each_file.endswith('.png')):
        all_images_array.append(each_file)
results_array=multiple_prediction.predictMultipleImages(all_images_array,result_count_per_image=3)
for each_result in results_array:
    predictions,percentage_probanlities=each_result['predictions'],each_result['percentage_probabilities']
    for index in range(len(predictions)):
        print(predictions[index]+':'+str(percentage_probanlities[index]))
print('-----------')

#目标检测
from imageai.Detection import ObjectDetection
import os
import time
start_time=time.time()
#execution_path=os.getcwd()#获取当前目录
detector=ObjectDetection() #实例化一个ObjectDetection类
detector.setModelTypeAsRetinaNet() #设置算法模型类型为RetinaNet
#etector.setModelPath()
detector.loadModel() #加载模型
#图像目标检测，百分比概率阈值设置为30可检测出更多的物体（默认值为30）
detections=detector.detectObjectsFromImage(input_image="D:\Image\\four.jpg",output_image_path='D:\Image\\fourr.jpg',minimum_percentage_probability=30)
end_time=time.time()
for eachObject in detections:
    print(eachObject['name'],":",eachObject['percentage_probability'],":",eachObject['box_points'])
print('Total Time cost:',end_time-start_time)

#视频目标检测
from imageai.Detection import VideoObjectDetection
import os
import time
start_time=time.time()
detector=VideoObjectDetection() #初始化视频检测类
detector.setModelTypeAsRetinaNet()
#detector.setModelPath('D:\Image:\haha.mp4')
detector.loadModel() #加载模型
video_path=detector.detectObjectsFromVideo(input_file_path='D:\Image\haha.mp4',output_file_path='D:Image:\hahaha.mp4',frames_per_second=20,log_progress=True)
print(video_path)
end_time=time.time()
print('Total time cost:',end_time-start_time)