05-09 03:38 阅读 204

开源图像识别、imageai图像识别、对象识别、识别人、车、猫、狗等80种简易版

imageai官网地址：传送门

详细版地址：https://blog.csdn.net/Baldprogrammer/article/details/116358180

项目地址：传送门

1、安装环境（最终版）

安装python-3.6.8-amd64 一键安装，勾选添加到path中

安装imageAI --官网地址：https://imageai-cn.readthedocs.io/zh_CN/latest/ImageAI.html#id1

cmd执行：

pip3 install --index-url https://pypi.douban.com/simple -U tensorflow==1.4.0

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U numpy==1.16.0

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U scipy==1.4.1

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U opencv-python==4.5.1.48

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U pillow==8.2.0

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U matplotlib==3.3.4

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U h5py==2.10.0

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -U keras==2.2.0

pip3 install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.1/imageai-2.0.1-py3-none-any.whl

模型文件下载地址：传送门

图像预测:

from imageai.Detection import ObjectDetection

import os

# os.environ["TF_CPP_MIN_LOG_LEVEL"]='1' # 这是默认的显示等级，显示所有信息

# os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' # 只显示 warning 和 Error

# os.environ["TF_CPP_MIN_LOG_LEVEL"]='3' # 只显示 Error

execution_path = os.getcwd()

detector = ObjectDetection()

detector.setModelTypeAsRetinaNet()

detector.setModelPath(os.path.join(execution_path+"\\model\\" , "resnet50_coco_best_v2.0.1.h5"))

# 设置模型的检测速度 detection_speed="fast" “normal”(default), “fast”, “faster” , “fastest” and “flash”

detector.loadModel(detection_speed="normal")

# 定义使用的模型

custom_objects = detector.CustomObjects( person=True, bicycle=True, car=True, motorcycle=True, airplane=True,

bus=True, train=True, truck=True, boat=True, traffic_light=True, fire_hydrant=True, stop_sign=True,

parking_meter=True, bench=True, bird=True, cat=True, dog=True, horse=True, sheep=True, cow=True, elephant=True, bear=True, zebra=True,

giraffe=True, backpack=True, umbrella=True, handbag=True, tie=True, suitcase=True, frisbee=True, skis=True, snowboard=True,

sports_ball=True, kite=True, baseball_bat=True, baseball_glove=True, skateboard=True, surfboard=True, tennis_racket=True,

bottle=True, wine_glass=True, cup=True, fork=True, knife=True, spoon=True, bowl=True, banana=True, apple=True, sandwich=True, orange=True,

broccoli=True, carrot=True, hot_dog=True, pizza=True, donot=True, cake=True, chair=True, couch=True, potted_plant=True, bed=True,

dining_table=True, toilet=True, tv=True, laptop=True, mouse=True, remote=True, keyboard=True, cell_phone=True, microwave=True,

oven=True, toaster=True, sink=True, refrigerator=True, book=True, clock=True, vase=True, scissors=True, teddy_bear=True, hair_dryer=True,

toothbrush=True)

# 是否把识别对象抠出来放到文件夹里

isSingle=True

inPath=execution_path+"\\image\\in\\"

outPath=execution_path+"\\image\\out\\"

all_images_array = []

all_files = os.listdir(inPath)

for each_file in all_files:

if(each_file.endswith(".jpg") or each_file.endswith(".png")):

all_images_array.append(each_file)

for path in all_images_array:

# 进行识别

detections, objects_path = detector.detectCustomObjectsFromImage(

extract_detected_objects=isSingle, # 将识别的图片单独保存出来

minimum_percentage_probability=50, # 置信度

input_type="file", # 输入文件格式 file/array/stream

output_type="file", # 输出文件格式 file/array/stream

custom_objects=custom_objects, # 选择可用模型

input_image=os.path.join(inPath, path), # 输入文件

output_image_path=os.path.join(outPath, "copy_"+path), # 输出文件

)

# 输出识别信息

if isSingle:

for eachObject, eachObjectPath in zip(detections, objects_path):

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

print("Object's image saved in " + eachObjectPath)

else:

for eachObject in detections:

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

print("--------------------------------")

对象检测