開源影像識別、imageai影像識別、物件識別、識別人、車、貓、狗等80種 簡易版

imageai官網地址： 傳送門

詳細版地址：https://blog.csdn.net/Baldprogrammer/article/details/116358180

專案地址：傳送門

1、安裝環境（最終版）
安裝python-3.6.8-amd64 一鍵安裝，勾選添加到path中

安裝imageAI --官網地址：https://imageai-cn.readthedocs.io/zh_CN/latest/ImageAI.html#id1
cmd執行：
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

模型檔案下載地址：傳送門

影像預測:

from imageai.Detection import ObjectDetection
import os

# os.environ["TF_CPP_MIN_LOG_LEVEL"]='1' # 這是默認的顯示等級，顯示所有資訊
# os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' # 只顯示 warning 和 Error
# os.environ["TF_CPP_MIN_LOG_LEVEL"]='3' # 只顯示 Error

execution_path = os.getcwd()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()


detector.setModelPath(os.path.join(execution_path+"\\model\\" , "resnet50_coco_best_v2.0.1.h5"))
# 設定模型的檢測速度 detection_speed="fast" “normal”(default), “fast”, “faster” , “fastest” and “flash”
detector.loadModel(detection_speed="normal")


# 定義使用的模型
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)


# 是否把識別物件摳出來放到檔案夾里
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:
    # 進行識別
    detections, objects_path = detector.detectCustomObjectsFromImage(

        extract_detected_objects=isSingle,  # 將識別的圖片單獨保存出來

        minimum_percentage_probability=50,  # 置信度

        input_type="file",  # 輸入檔案格式 file/array/stream

        output_type="file",  # 輸出檔案格式 file/array/stream

        custom_objects=custom_objects,  # 選擇可用模型

        input_image=os.path.join(inPath, path),  # 輸入檔案

        output_image_path=os.path.join(outPath, "copy_"+path),  # 輸出檔案

    )

    # 輸出識別資訊
    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("--------------------------------")

物件檢測

from imageai.Detection import ObjectDetection
import os

# os.environ["TF_CPP_MIN_LOG_LEVEL"]='1' # 這是默認的顯示等級，顯示所有資訊
# os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' # 只顯示 warning 和 Error
# os.environ["TF_CPP_MIN_LOG_LEVEL"]='3' # 只顯示 Error

execution_path = os.getcwd()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()


detector.setModelPath(os.path.join(execution_path+"\\model\\" , "resnet50_coco_best_v2.0.1.h5"))
# 設定模型的檢測速度 detection_speed="fast" “normal”(default), “fast”, “faster” , “fastest” and “flash”
detector.loadModel(detection_speed="normal")


# 定義使用的模型
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)


# 是否把識別物件摳出來放到檔案夾里
isSingle=True


# 進行識別
detections, objects_path  = detector.detectCustomObjectsFromImage(

                    extract_detected_objects=isSingle, #將識別的圖片單獨保存出來

                    minimum_percentage_probability=50, #置信度

                    input_type="file", #輸入檔案格式 file/array/stream

                    output_type="file", #輸出檔案格式 file/array/stream

                    custom_objects=custom_objects,  #選擇可用模型

                    input_image=os.path.join(execution_path+"\\image\\in\\", "13.jpg"), # 輸入檔案

                    output_image_path=os.path.join(execution_path+"\\image\\out\\", "13(2).jpg"), # 輸出檔案

                    )



#輸出識別資訊
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("--------------------------------")

視頻檢測

from imageai.Detection import VideoObjectDetection
import os

# os.environ["TF_CPP_MIN_LOG_LEVEL"]='1' # 這是默認的顯示等級，顯示所有資訊
# os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' # 只顯示 warning 和 Error
# os.environ["TF_CPP_MIN_LOG_LEVEL"]='3' # 只顯示 Error

execution_path = os.getcwd()

detector = VideoObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath(os.path.join(execution_path+"\\model\\", "resnet50_coco_best_v2.0.1.h5"))
# 模型等級 “normal”(default), “fast”, “faster” , “fastest” and “flash”
detector.loadModel(detection_speed="fastest")



# 定義使用的模型
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)


video_path = detector.detectCustomObjectsFromVideo(custom_objects=custom_objects, #指定模型
                                                   input_file_path=os.path.join(execution_path+"\\video\\in\\", "holo1.mp4"),  #輸入輸入視頻路徑
                                                   output_file_path=os.path.join(execution_path+"\\video\\out\\", "holo1_1"),  #不指定默認為avi格式
                                                   frames_per_second=20, #輸出視頻的幀數
                                                   frame_detection_interval=5,#幀間隔 每隔幾幀檢測一回
                                                   log_progress=True) #輸出日志


print(video_path)

效果圖: