class StageRecognizer():
def __init__(self, trained_net_path):
self.net = NeuralNet()
self.net.load_from_file(trained_net_path)
def recognize_image(self, img):
net_return = self.net.apply_over_data(extract_counter_feat(img))
stage_number = int(round(net_return))
stage = ''
precision = 'strong'
if stage_number == 1:
stage = 'red'
if abs(stage_number - 1) > .15:
precision = 'weak'
elif stage_number == 2:
stage = 'yellow'
if abs(stage_number - 1) > .15:
precision = 'weak'
elif stage_number == 3:
stage = 'green'
if abs(stage_number - 1) > .15:
precision = 'weak'
return stage, precision
from neuralnet import NeuralNet
from extractfeatures import *
import cv2
if __name__ == "__main__":
red = cv2.imread('/home/hoshiro/Pictures/test-img/red-light.jpg', cv2.CV_LOAD_IMAGE_COLOR)
yellow = cv2.imread('/home/hoshiro/Pictures/test-img/yellow-light.jpg', cv2.CV_LOAD_IMAGE_COLOR)
green = cv2.imread('/home/hoshiro/Pictures/test-img/green-light.jpg', cv2.CV_LOAD_IMAGE_COLOR)
features_red = extract_hist_features(red)
features_yellow = extract_hist_features(yellow)
features_green = extract_hist_features(green)
neural_net = NeuralNet()
neural_net.build(len(features_red), len(features_red) / 2, 1)
neural_net.create_data_set()
neural_net.add_list_of_data([features_red], 1)
neural_net.add_list_of_data([features_yellow], 2)
neural_net.add_list_of_data([features_green], 3)
neural_net.train()
print neural_net.apply_over_data(features_yellow)
