def main(): myImager = Imager() #myModel = Model() while True: img = myImager.process_img(myImager.capture_img()) if not img is None: myImager.show_img(img)
def __init__(self, model_name_path, input_size, labels, num_requests=2): self.model = model_name_path + '.xml' self.weights = model_name_path + '.bin' self.labels = labels self.input_size = input_size self.imer = Imager(self.input_size, self.labels) if not os.path.exists(self.model) or not os.path.exists(self.weights): raise ValueError( 'model files {} does not exist.'.format(model_name_path)) self.plugin = IEPlugin(device='MYRIAD') log.info('Loading network files:\n\t{}\n\t{}'.format( self.model, self.weights)) self.net = IENetwork(model=self.model, weights=self.weights) log.info('Preparing inputs') self.input_blob = next(iter(self.net.inputs)) self.net.batch_size = 1 log.info('Loading model to the plugin') self.current_request_id = 0 self.next_request_id = 1 self.num_requests = num_requests self.exec_net = self.plugin.load(network=self.net, num_requests=self.num_requests)
""" rest api with flask for get birb photos, i/o users stuff, and the souls of the lost childrens """ from flask import Flask from flask_restful import Resource, Api from imager import Imager # IGNORE THIS BULLSHIT, JUST IGNORE PLEASE import sys sys.path.append("..") app = Flask(__name__) api = Api(app) # test img = Imager() files = img.get_files() class Bot(Resource): def get(self, id): return {'msg': f"I f****d your mom {id} times"} # change this api.add_resource(Bot, '/img/<string:id>') if __name__ == '__main__': app.run(debug=True)
from imager import Imager from imager import ptest2 from os import path if __name__ == "__main__": #testImage = ptest2() #Nothing's like a trippy Einstein-pic to get things going. #Testing contrast method testImage = Imager(fid=path.normpath("images/einstein.gif")) bildeMedTekst = testImage.write_text().show()
def __init__(self, model_type, model_file, anchor_file, num_classes, input_size, labels, is_training=False): if model_type not in self.model_types: raise ValueError( 'model_type can only be either \'full\' or \'tiny\'.') elif not model_type: model_type = self.model_types[0] self.model_type = model_type if not model_file: model_file = './data/bin/{}'.format( self.default_models.get(model_type)) elif not os.path.exists(model_file): raise ValueError( 'model file {} does not exist.'.format(model_file)) self.model_file = model_file if '.pb' not in self.model_file: self.frozen_filename = '_'.join( ['frozen', os.path.basename(self.model_file).split('.')[0]]) self.frozen_filename = self.freeze_dir + self.frozen_filename + '.pb' if not input_size: input_size = 416 if type(input_size) is int: self.input_size = input_size, input_size else: self.input_size = input_size self.labels = labels self.imer = Imager(self.input_size, self.labels) if os.path.exists(self.frozen_filename): self.defrost() self.input = tf.get_default_graph().get_tensor_by_name( 'import/input:0') self.output = tf.get_default_graph().get_tensor_by_name( 'import/detections/output:0') else: if not anchor_file: anchor_file = 'data/anchors/' + self.model_type + '.txt' elif not os.path.exists(anchor_file): raise ValueError( '{} anchor file does not exist.'.format(anchor_file)) self.anchor_file = anchor_file self.num_classes = num_classes self.is_training = is_training self.input = tf.placeholder( tf.float32, [None, self.input_size[0], self.input_size[1], 3], 'input') self.model = self.tf_models[self.model_type](self.input, self.num_classes, self.input_size, self.anchor_file, self.is_training) with tf.variable_scope('detections'): self.output = self.model.graph() self.loader = WeightLoader(tf.global_variables('detections'), self.model_file) # self.sess.run(tf.global_variables_initializer()) self.sess.run(self.loader.load_now()) self.freeze()
from walker import Walker from imager import Imager w = Walker([0, 0], [-256, 256, -256, 256]) w.random_walk() imgGen = Imager(w) imgGen.generate_linear_gradient("./generated/1.png") imgGen.generate_linear_gradient("./generated/2.png", mode="HSV", start_color=(50, 200, 200), end_color=(200, 255, 255)) imgGen.generate_linear_gradient("./generated/3.png", mode="RGB", bg_color=(0, 0, 0, 0), start_color=(255, 0, 0, 0), end_color=(0, 255, 0, 255)) imgGen.generate_linear_gradient("./generated/4.png", mode="HSV", bg_color=(0, 0, 0), start_color=(0, 255, 255), end_color=(255, 255, 255)) w.save("./generated/1.walker")
import datetime # Sample usage file name3 = 'wahrsis3' center3 = [1724, 2592] radius3 = 1470 relativePosition3 = np.array([0, 0, 0]) calibRot3 = np.array([[0.99555536, 0.09404159, 0.00506982], [-0.09393761, 0.99541774, -0.01786745], [-0.00672686, 0.01731178, 0.99982751]]) calibTrans3 = np.array([[0.00552915], [0.00141732], [0.00553584]]) longitude3 = '103:40:49.9' lattitude3 = '1:20:35' altitude3 = 59 wahrsis3 = Imager(name3, center3, radius3, relativePosition3, calibRot3, calibTrans3, longitude3, lattitude3, altitude3) name4 = 'wahrsis4' center4 = [2000, 2975] radius4 = 1665 relativePosition4 = np.array([-2.334, 101.3731, -8.04]) calibRot4 = np.array([[0.9710936, -0.23401871, 0.04703662], [0.234924, 0.97190314, -0.01466276], [-0.04228367, 0.02528894, 0.99878553]]) calibTrans4 = np.array([[-0.00274625], [-0.00316865], [0.00516088]]) wahrsis4 = Imager(name4, center4, radius4, relativePosition4, calibRot4, calibTrans4) images3 = [ cv2.imread('wahrsis3/2015-10-29-12-58-01-wahrsis3-low.jpg'), cv2.imread('wahrsis3/2015-10-29-12-58-01-wahrsis3-med.jpg'),
import json from imager import Imager from os import listdir, getcwd, walk from os.path import isfile, join inDir = input('Directories -\n{}\nInput directory: '.format( next(walk('.'))[1])) myImager = Imager() dataset = [] print('Processing data') for picFile in [ fileName if fileName.endswith('.jpg') else '' for fileName in listdir(getcwd() + '/' + inDir) ]: rawImg = myImager.open_img(inDir + '/' + picFile) procdImg = myImager.process_img(rawImg) if procdImg is None: continue #myImager.show_img(procdImg) dataset.append([procdImg.tolist(), int(picFile.split()[0].split('.')[0]) ]) #picFile.split()[0]]) print('Writing to dataset file') with open(inDir + '-dataset.json', 'w') as dsFile: dsFile.write(json.dumps(dataset)) print('Sucessfully wrote data set to JSON file')