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')
