我尝试了下面提供的代码来检测摄像机中的数字，并在它周围放置一个轮廓，然后使用H5模型进行分类，但它给了我不好的结果，只是相机是打开的，我可以看到既没有检测也没有分类。我不知道我需要改变或工作。
我使用Python 2.7、OpenCV 4.2.0和TensorFlow 1.5.0
我正在使用的代码：

from statistics import mode
import cv2, time
from keras.models import load_model
from keras.datasets import mnist
import tensorflow as tf
import numpy as np
import vision_definitions
from PIL import Image
import numpy as np
import sys, os

from utils.inference import detect_digits
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input

# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/model.sav'
class_model_path = '../trained_models/class_models/Num.h5'

# hyper-parameters for bounding boxes shape
frame_window = 10
class_offsets = (20, 40)

# loading models
digit_detection = load_detection_model(detection_model_path)
class_classifier = load_model(class_model_path)

# getting input model shapes for inference
class_target_size = class_classifier.input_shape[1:3]

class_window = []
class_window1 = []

# starting video streaming
cameraIndex = 0
resolution = vision_definitions.kVGA
colorSpace = vision_definitions.kRGBColorSpace
resolution = 2
colorSpace = 3
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
if video_capture.isOpened():
 frame = video_capture.read()
else:
 rval = False
while True:
    rval, frame = video_capture.read()
    gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    digits = detect_digits(digit_detection, gray_image)
    frame = cv2.resize(frame, (640, 480))
    key = cv2.waitKey(1)
    b,g,r = cv2.split(frame) # get b,g,r
    rgb_img = cv2.merge([r,g,b]) # switch it to rgb    

    for digit_coordinates in digits:
        x1, x2, y1, y2 = apply_offsets(digit_coordinates, class_offsets)
        gray_digit = gray_image[y1:y2, x1:x2]
        try:
            gray_digit = cv2.resize(gray_digit, (class_target_size))
        except:
            continue

        gray_digit = preprocess_input(gray_digit, True)
        gray_digit = np.expand_dims(gray_digit, 0)
        gray_digit = np.expand_dims(gray_digit, -1)
        class_prediction = class_classifier.predict(gray_digit)
        class_probability = np.max(class_prediction)
        class_label_arg = np.argmax(class_prediction)

       
        color = color.astype(int)
        color = color.tolist()

        draw_bounding_box(digit_coordinates, rgb_image, color)
        draw_text(digit_coordinates, rgb_image, class_mode,
                  color, 0, -45, 1, 1)

    frame = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
    cv2.imshow('window_frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break