is_military.py

# USAGE
# python pi_deep_learning.py --prototxt models/bvlc_googlenet.prototxt --model models/bvlc_googlenet.caffemodel --labels synset_words.txt --image images/barbershop.png
# python pi_deep_learning.py --prototxt models/squeezenet_v1.0.prototxt --model models/squeezenet_v1.0.caffemodel --labels synset_words.txt --image images/barbershop.png

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

# construct the argument parse and parse the arguments

def main():
	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())

	# 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]

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

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


def predict(image, net, classes):
	# 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))

	# 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
	preds = preds.reshape((1, len(classes)))
	idxs = np.argsort(preds[0])[::-1]

	# loop over the top-5 predictions and display them
	military = "military uniform"
	print(classes[idxs[0]])
	if classes[idxs[0]] == military:
		print("Found a {}".format(military))

	for i, idx in enumerate(idxs):
		# draw the top prediction on the input image
		
		# display the predicted label + associated probability to the
		# console	
		if classes[idx] == military:
			return True
		if i == 5:
			break
	return False

	# 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)
	# 		if classes[idx] == military:
	# 			is_military = True
	# 		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)

if __name__ == "__main__":
	main()