Opencv，错误：需要以下参数：-i /-image，-p /-prototxt，-m /-model

Question

尝试在Mac终端上执行代码时收到此错误消息

$ python detect_faces.py --image IMG_4218.jpg

用法：detect_faces.py [-h] -i图像-p PROTOTXT -m模型[-c机密] detect_faces.py：错误：需要以下参数：-p /-prototxt，-m /-model

我是否在Argparse部分中缺少某些参数？请帮助我，谢谢！

dectect_faces.py代码

# import necessary package

import numpy as np
import argparse
import cv2

# parsing arguments: path to input image; Caffe prototxt file; pretrained Caffe model
# overwrite the default threshold of 0.5 if u wish

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument("-p", "--prototxt", required=True, help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")

args = vars(ap.parse_args())

# model & blob
# 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
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image,(300, 300)), 1.0, (300,300),(104.0, 177.0, 123.0))

# 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 range(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"]:

        # compute the (x, y)-coordinates of the bounding box for the
        # object
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")

        # draw the bounding box of the face along with the associated
        # probability
        text = "{:.2f}%".format(confidence * 100)
        y = startY - 10 if startY - 10 > 10 else startY + 10
        cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
        cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)

Answer 1

应传递所有必需的参数，即：

"--image", required=True
"--prototxt", required=True
"--model", required=True


$ python detect_faces.py --image IMG_4218.jpg --prototxt "path to Caffe 'deploy' prototxt file" --model "path to Caffe pre-trained model"