当我尝试在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:以下参数是 必需:-p /-prototxt,-m /-model
我在Argparse部分缺少一些参数吗?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)
答案 0 :(得分:0)
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 deploy.prototxt --model model_name.caffemodel