我最近刚接触openCV的内容，在win10的pycharm里面试着去运行相关的程序，但是遇到了报错，可能问题很小白，希望各位大牛不吝赐教。其内容是：deep-learning-object-detection.py: error: the following arguments are required:
全篇代码如下
# USAGE
# python deep_learning_object_detection.py --image images/example_01.jpg \
# --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
# import the necessary packages
import numpy as np
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", r"--C:\Users\52314\Desktop\deep\images\example_01.jpg", required=True,
help="path to input image")
ap.add_argument("-p", r"--C:\Users\52314\Desktop\deepMobileNetSSD_deploy.prototxt.txt", required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", r"--C:\Users\52314\Desktop\deep\deep_learning_object_detection.py", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(Args[prototxt], Args[model])
# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
image = cv2.imread(Args["image"])
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > Args["confidence"]:
# extract the index of the class label from the `detections`,
# then compute the (x, y)-coordinates of the bounding box for
# the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# display the prediction
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
print("[INFO] {}".format(label))
cv2.rectangle(image, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)
我在网上看到说argparse在win10上面兼容不好所以换了个表达方式，那个也不行，那么到底是什么问题呢？要如何解决这个问题呢？非常感谢！
