Question

我已经使用python创建了一个带有tensorflow的程序，然后我使用pyinstaller构建了一个exe，首先我使用了这一行：

pyinstaller test.py

然后，因为它给了我模块tensorflow.python._pywrap_tensorflow_internal.pyd的导入错误我将此文件复制到名为tensorflow \ python的dist \ test \目录的新文件夹中。然后我用这条线重复建筑物：

pyinstaller test.py -F --add-data "C:\path\dist\test\tensorflow\python\tensorflow.python._pywrap_tensorflow_internal.pyd";"C:\path\dist"

现在导入错误消失了，但是程序仍然崩溃并且我遇到了这个错误：

C：\ path \ dist \ tensorflow.python._pywrap_tensorflow_internal.pyd无法提取

打开：无效的参数

该问题的解决方案是什么？

我正在尝试构建的脚本是这样的：

import pyscreenshot as ImageGrab
from   win32api import GetSystemMetrics
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
import warnings
import h5py
import time

def Condition(data):
    global name
    for ch in data:
        if ch['name'] != name:
            print("data name:",ch['name'])
            name = ch['name']
            UpdateLabels(data, labels)


def GetName(data, name):
    for ch in data:
        name = ch['name']
        return name

def UpdateLabels(data, labels):

    for ch in data:
        if ch['name'] == "ace":
            labels["ace"] += 1
        elif ch['name'] == "king":
            labels["king"] += 1
        elif ch['name'] == "queen":
            labels["queen"] += 1
        elif ch['name'] == "jack":
            labels["jack"] += 1
        elif ch['name'] == "ten":
            labels["ten"] += 1
        elif ch['name'] == "nine":
            labels["nine"] += 1
        elif ch['name'] == "eight":
            labels["eight"] += 1
        elif ch['name'] == "seven":
            labels["seven"] += 1
        elif ch['name'] == "six":
            labels["six"] += 1
        elif ch['name'] == "five":
            labels["five"] += 1
        elif ch['name'] == "four":
            labels["four"] += 1
        elif ch['name'] == "three":
            labels["three"] += 1
        elif ch['name'] == "two":
            labels["two"] += 1
    return labels

def UpdateCounter(data, c):
    for ch in data:
        if ch['name'] == "ace":
            c = c - labels["ace"]
        if ch['name'] == "king":
            c = c - labels["king"]
        if ch['name'] == "queen":
            c = c - labels["queen"]
        if ch['name'] == "jack":
            c = c - labels["jack"]
        if ch['name'] == "ten":
            c = c - labels["ten"]
        if ch['name'] == "six":
            c = c + labels["six"]
        if ch['name'] == "five":
            c = c + labels["five"]
        if ch['name'] == "four":
            c = c + labels["four"]
        if ch['name'] == "three":
            c = c + labels["three"]
        if ch['name'] == "two":
            c = c + labels["two"]
        return c

if __name__ == '__main__':


    sys.path.append("..")
    from utils import label_map_util
    from utils import visualization_utils as vis_util

    MODEL_NAME = 'inference_graph'
    IMAGE_NAME = 'test1.jpg'
    CWD_PATH = os.getcwd()
    PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
    PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')
    PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)

    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

    NUM_CLASSES = 13

    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
    category_index = label_map_util.create_category_index(categories)
    detection_graph = tf.Graph()

    with detection_graph.as_default():

        od_graph_def = tf.GraphDef()

        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
            sess = tf.Session(graph=detection_graph)

    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')

    c = 0
    labels = {"ace" : 0, "king": 0, "queen": 0, "jack": 0, "ten": 0, "nine": 0, "eight": 0,"seven": 0, "six": 0, "five": 0, "four":0, "three": 0, "two": 0}
    name = None

    while True:


        with warnings.catch_warnings():
            warnings.filterwarnings("ignore",category=FutureWarning)

        screenshot=ImageGrab.grab(bbox=(42,42, GetSystemMetrics(0),GetSystemMetrics(1)))
        screenshot.save(IMAGE_NAME)
        time.sleep(1)

        image = cv2.imread(PATH_TO_IMAGE)
        image_expanded = np.expand_dims(image, axis=0)

        (scores, classes, num) = sess.run(
            [detection_scores, detection_classes, num_detections],
            feed_dict={image_tensor: image_expanded})

        data = [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.98]


        Condition(data)
        print(UpdateCounter(data, c))

pyinstaller崩溃时生成的exe并给出错误->无法提取_pywrap_tensorflow_internal fopen无效参数

0 个答案: