deep_learning_with_opencv.py

# USAGE
# python deep_learning_with_opencv.py --image images/jemma.png --prototxt bvlc_googlenet.prototxt --model bvlc_googlenet.caffemodel --labels synset_words.txt

# import the necessary packages
import numpy as np
import argparse
import time
import cv2

# construct the argument parse and parse the arguments 创建参数解析器，从命令行解析参数
'''
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("-l", "--labels", required=True,
	help="path to ImageNet labels (i.e., syn-sets)")
args = vars(ap.parse_args())
'''

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default='./images/eagle.png',
	help="path to input image")
ap.add_argument("-p", "--prototxt", default='./bvlc_googlenet.prototxt',
	help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default='./bvlc_googlenet.caffemodel',
	help="path to Caffe pre-trained model")
ap.add_argument("-l", "--labels", default='./synset_words.txt',
	help="path to ImageNet labels (i.e., syn-sets)")
args = vars(ap.parse_args())





# load the input image from disk
image = cv2.imread(args["image"])

# load the class labels from disk
rows = open(args["labels"]).read().strip().split("\n")
classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]

# our CNN requires fixed spatial dimensions for our input image(s)
# so we need to ensure it is resized to 224x224 pixels while
# performing mean subtraction (104, 117, 123) to normalize the input;
# after executing this command our "blob" now has the shape:
# (1, 3, 224, 224)
blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123))

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# set the blob as input to the network and perform a forward-pass to
# obtain our output classification
net.setInput(blob)
start = time.time()
preds = net.forward()
end = time.time()
print("[INFO] classification took {:.5} seconds".format(end - start))

# sort the indexes of the probabilities in descending order (higher
# probabilitiy first) and grab the top-5 predictions
idxs = np.argsort(preds[0])[::-1][:5]

# loop over the top-5 predictions and display them
for (i, idx) in enumerate(idxs):
	# draw the top prediction on the input image
	if i == 0:
		text = "Label: {}, {:.2f}%".format(classes[idx],
			preds[0][idx] * 100)
		cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
			0.7, (0, 0, 255), 2)

	# display the predicted label + associated probability to the
	# console	
	print("[INFO] {}. label: {}, probability: {:.5}".format(i + 1,
		classes[idx], preds[0][idx]))

# display the output image
cv2.imshow("Image", image)
cv2.waitKey(0)