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Python 3.x 如何使用多线程"；ImageAI"；模块'；通过图像方法检测对象？

python-3.x multithreading

Python 3.x 如何使用多线程"；ImageAI"；模块'；通过图像方法检测对象？,python-3.x,multithreading,object-detection,Python 3.x,Multithreading,Object Detection,我设法获得以下代码，从一个目录中获取一个图像列表，使用对其应用对象检测，并将“已处理”的图像保存到一个新目录中。当我只指定一个额外的线程来运行它时，使用下面的代码就可以很好地工作 当我尝试增加线程范围时，它开始抛出以下异常： 无法将feed_dict键解释为张量：张量 张量（“占位符：0”，shape=（7,7,3,64），dtype=float32）不是一个 这个图的元素 我在中找不到任何说明多线程是不可能的东西 一些额外的控制台输出，以防有用（这是尝试5个线程时的输出）： 单线程的工作代码：

我设法获得以下代码，从一个目录中获取一个图像列表，使用对其应用对象检测，并将“已处理”的图像保存到一个新目录中。当我只指定一个额外的线程来运行它时，使用下面的代码就可以很好地工作

当我尝试增加线程范围时，它开始抛出以下异常：

无法将feed_dict键解释为张量：张量 张量（“占位符：0”，shape=（7,7,3,64），dtype=float32）不是一个 这个图的元素

我在中找不到任何说明多线程是不可能的东西

一些额外的控制台输出，以防有用（这是尝试5个线程时的输出）：

单线程的工作代码：

import os
import cv2
import threading
import subprocess
import tensorflow as tf

from queue import Queue

from imageai.Detection import ObjectDetection

cwd = os.getcwd()
image_dir = os.path.join(cwd, 'TrainingImages')
processed_dir = os.path.join(cwd, 'ProcessedImages')

def process_image(image_filename):
    detector = ObjectDetection()
    detector.setModelTypeAsRetinaNet()
    detector.setModelPath(os.path.join(cwd, "resnet50_coco_best_v2.0.1.h5"))
    detector.loadModel(detection_speed="fast")

    if image_filename.endswith(".JPG"):
        try:
            detections = detector.detectObjectsFromImage(input_image=os.path.join(image_dir, image_filename), output_image_path=os.path.join(processed_dir, 'processed_' + image_filename))
            return detections
        except ValueError as e:
            print("Error processing: {}: {}".format(os.path.join(image_dir, image_filename), e))
            return False


def worker(q):
    ''' The worker thread pulls an item from the queue and processes it '''
    while True:
        image_filename = q.get()
        detections = process_image(image_filename)

        if detections:
            for obj in detections:
                print("{} : {}".format(obj["name"], obj["percentage_probability"]))
        else:
            print("No objects detected")

        q.task_done()


if __name__ == '__main__':
    # Get all files in image directory
    image_list = os.listdir(image_dir)

    # Create Queue for images
    q = Queue()

    for image_filename in image_list:
        q.put(image_filename)

    for i in range(1):
        t = threading.Thread(target=worker, args=(q, ))
        t.daemon = True
        t.start()

    q.join()

Python version 3.6.5
Package             Version
------------------- ---------
absl-py             0.6.1
astor               0.7.1
astroid             1.6.4
certifi             2018.4.16
chardet             3.0.4
colorama            0.3.9
cycler              0.10.0
gast                0.2.0
grpcio              1.17.1
h5py                2.9.0
idna                2.6
imageai             2.0.2
isort               4.3.4
Keras               2.2.4
Keras-Applications  1.0.6
Keras-Preprocessing 1.0.5
kiwisolver          1.0.1
lazy-object-proxy   1.3.1
Markdown            3.0.1
matplotlib          3.0.2
mccabe              0.6.1
numpy               1.15.4
opencv-python       3.4.5.20
Pillow              5.4.0
pip                 18.1
pipenv              2018.5.18
protobuf            3.6.1
pylint              1.9.1
pyparsing           2.3.0
python-dateutil     2.7.5
PyYAML              3.13
requests            2.18.4
scipy               1.2.0
setuptools          39.0.1
six                 1.11.0
tensorboard         1.12.1
tensorflow-gpu      1.12.0
termcolor           1.1.0
urllib3             1.22
virtualenv          16.0.0
virtualenv-clone    0.3.0
Werkzeug            0.14.1
wheel               0.32.3
wrapt               1.10.11
项目利用了以下内容：

import os
import cv2
import threading
import subprocess
import tensorflow as tf

from queue import Queue

from imageai.Detection import ObjectDetection

cwd = os.getcwd()
image_dir = os.path.join(cwd, 'TrainingImages')
processed_dir = os.path.join(cwd, 'ProcessedImages')

def process_image(image_filename):
    detector = ObjectDetection()
    detector.setModelTypeAsRetinaNet()
    detector.setModelPath(os.path.join(cwd, "resnet50_coco_best_v2.0.1.h5"))
    detector.loadModel(detection_speed="fast")

    if image_filename.endswith(".JPG"):
        try:
            detections = detector.detectObjectsFromImage(input_image=os.path.join(image_dir, image_filename), output_image_path=os.path.join(processed_dir, 'processed_' + image_filename))
            return detections
        except ValueError as e:
            print("Error processing: {}: {}".format(os.path.join(image_dir, image_filename), e))
            return False


def worker(q):
    ''' The worker thread pulls an item from the queue and processes it '''
    while True:
        image_filename = q.get()
        detections = process_image(image_filename)

        if detections:
            for obj in detections:
                print("{} : {}".format(obj["name"], obj["percentage_probability"]))
        else:
            print("No objects detected")

        q.task_done()


if __name__ == '__main__':
    # Get all files in image directory
    image_list = os.listdir(image_dir)

    # Create Queue for images
    q = Queue()

    for image_filename in image_list:
        q.put(image_filename)

    for i in range(1):
        t = threading.Thread(target=worker, args=(q, ))
        t.daemon = True
        t.start()

    q.join()

Python version 3.6.5
Package             Version
------------------- ---------
absl-py             0.6.1
astor               0.7.1
astroid             1.6.4
certifi             2018.4.16
chardet             3.0.4
colorama            0.3.9
cycler              0.10.0
gast                0.2.0
grpcio              1.17.1
h5py                2.9.0
idna                2.6
imageai             2.0.2
isort               4.3.4
Keras               2.2.4
Keras-Applications  1.0.6
Keras-Preprocessing 1.0.5
kiwisolver          1.0.1
lazy-object-proxy   1.3.1
Markdown            3.0.1
matplotlib          3.0.2
mccabe              0.6.1
numpy               1.15.4
opencv-python       3.4.5.20
Pillow              5.4.0
pip                 18.1
pipenv              2018.5.18
protobuf            3.6.1
pylint              1.9.1
pyparsing           2.3.0
python-dateutil     2.7.5
PyYAML              3.13
requests            2.18.4
scipy               1.2.0
setuptools          39.0.1
six                 1.11.0
tensorboard         1.12.1
tensorflow-gpu      1.12.0
termcolor           1.1.0
urllib3             1.22
virtualenv          16.0.0
virtualenv-clone    0.3.0
Werkzeug            0.14.1
wheel               0.32.3
wrapt               1.10.11
问题：

import os
import cv2
import threading
import subprocess
import tensorflow as tf

from queue import Queue

from imageai.Detection import ObjectDetection

cwd = os.getcwd()
image_dir = os.path.join(cwd, 'TrainingImages')
processed_dir = os.path.join(cwd, 'ProcessedImages')

def process_image(image_filename):
    detector = ObjectDetection()
    detector.setModelTypeAsRetinaNet()
    detector.setModelPath(os.path.join(cwd, "resnet50_coco_best_v2.0.1.h5"))
    detector.loadModel(detection_speed="fast")

    if image_filename.endswith(".JPG"):
        try:
            detections = detector.detectObjectsFromImage(input_image=os.path.join(image_dir, image_filename), output_image_path=os.path.join(processed_dir, 'processed_' + image_filename))
            return detections
        except ValueError as e:
            print("Error processing: {}: {}".format(os.path.join(image_dir, image_filename), e))
            return False


def worker(q):
    ''' The worker thread pulls an item from the queue and processes it '''
    while True:
        image_filename = q.get()
        detections = process_image(image_filename)

        if detections:
            for obj in detections:
                print("{} : {}".format(obj["name"], obj["percentage_probability"]))
        else:
            print("No objects detected")

        q.task_done()


if __name__ == '__main__':
    # Get all files in image directory
    image_list = os.listdir(image_dir)

    # Create Queue for images
    q = Queue()

    for image_filename in image_list:
        q.put(image_filename)

    for i in range(1):
        t = threading.Thread(target=worker, args=(q, ))
        t.daemon = True
        t.start()

    q.join()

Python version 3.6.5
Package             Version
------------------- ---------
absl-py             0.6.1
astor               0.7.1
astroid             1.6.4
certifi             2018.4.16
chardet             3.0.4
colorama            0.3.9
cycler              0.10.0
gast                0.2.0
grpcio              1.17.1
h5py                2.9.0
idna                2.6
imageai             2.0.2
isort               4.3.4
Keras               2.2.4
Keras-Applications  1.0.6
Keras-Preprocessing 1.0.5
kiwisolver          1.0.1
lazy-object-proxy   1.3.1
Markdown            3.0.1
matplotlib          3.0.2
mccabe              0.6.1
numpy               1.15.4
opencv-python       3.4.5.20
Pillow              5.4.0
pip                 18.1
pipenv              2018.5.18
protobuf            3.6.1
pylint              1.9.1
pyparsing           2.3.0
python-dateutil     2.7.5
PyYAML              3.13
requests            2.18.4
scipy               1.2.0
setuptools          39.0.1
six                 1.11.0
tensorboard         1.12.1
tensorflow-gpu      1.12.0
termcolor           1.1.0
urllib3             1.22
virtualenv          16.0.0
virtualenv-clone    0.3.0
Werkzeug            0.14.1
wheel               0.32.3
wrapt               1.10.11
有人能帮助我理解如何对上面的代码进行多线程处理，以允许异步处理多个图像吗

我试着尽可能多地提供信息

一些额外的信息：它似乎仍然运行多个线程；但是，第1个线程之后的每个线程都会抛出上述错误。然后它继续处理第一个图像，然后本质上“挂起”（只是什么也不做）。我对这个术语还不够熟悉，还不知道张量“图”是什么意思，也不知道要进一步查找什么来排除故障

以下是5个线程的输出：

C:\Users\Me\Desktop\Project>cd c:\Users\Me\Desktop\Project && cmd /C "set "PYTHONIOENCODING=UTF-8" && set "PYTHONUNBUFFERED=1" && C:\Users\Me\AppData\Local\Programs\Python\Python36\python.exe c:\Users\Me\.vscode\extensions\ms-python.python-2018.12.1\pythonFiles\ptvsd_launcher.py --default --client --host localhost
--port 61076 c:\Users\Me\Desktop\Project\app.py "
Using TensorFlow backend.
2019-01-02 21:52:37.108429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1070 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:03:00.0
totalMemory: 8.00GiB freeMemory: 6.61GiB
2019-01-02 21:52:37.113537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-02 21:52:38.396715: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-02 21:52:38.399994: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-01-02 21:52:38.402189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-01-02 21:52:38.404115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6368
MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2019-01-02 21:52:38.419623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-02 21:52:38.422946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-02 21:52:38.425552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-01-02 21:52:38.427139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-01-02 21:52:38.428894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6368
MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2019-01-02 21:52:38.436241: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-02 21:52:38.439749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-02 21:52:38.442604: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-01-02 21:52:38.444264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-01-02 21:52:38.445908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6368
MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2019-01-02 21:52:38.460772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-02 21:52:38.463329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-02 21:52:38.466661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-01-02 21:52:38.468411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-01-02 21:52:38.470123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6368
MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2019-01-02 21:52:38.476201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-02 21:52:38.478613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-02 21:52:38.484133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-01-02 21:52:38.493996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-01-02 21:52:38.496042: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6368
MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
Backend TkAgg is interactive backend. Turning interactive mode on.
----Errors out on this line----

2019-01-02 22:23:53.417469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6368 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
Error thrown: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(7, 7, 3, 64), dtype=float32) is not an element of this graph.
Error thrown: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(7, 7, 3, 64), dtype=float32) is not an element of this graph.
Error thrown: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(7, 7, 3, 64), dtype=float32) is not an element of this graph.
Error thrown: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(7, 7, 3, 64), dtype=float32) is not an element of this graph.
person : 78.54841947555542
truck : 56.28065466880798
可能会提供一些关于多线程的信息