def __init__(self, fontScale=1.0):
print("YoloInference::__init__()")
print(
"===============================================================")
print("Initialising Yolo Inference with the following parameters: ")
print("")
self.classLabels = None
self.colors = None
self.nmsThreshold = 0.35
self.fontScale = float(fontScale)
self.fontThickness = 2
self.net = None
self.rgb = True
self.verbose = False
self.lastMessageSentTime = datetime.now()
# Read class names from text file
print(" - Setting Classes")
with open(classesFile, 'r') as f:
self.classLabels = [line.strip() for line in f.readlines()]
# Generate colors for different classes
print(" - Setting Colors")
self.colors = np.random.uniform(0,
255,
size=(len(self.classLabels), 3))
# Read pre-trained model and config file
print(" - Loading Model and Config")
darknet.performDetect(configPath=yolocfg,
weightPath=yoloweight,
metaPath=dataFile,
initOnly=True)
def __init__(
self,
fontScale = 1.0,
sendMessage = False):
logging.info('>> ' + self.__class__.__name__ + "." + sys._getframe().f_code.co_name + '()')
logging.info('===============================================================')
logging.info('Initializing Yolo Inference with the following parameters:')
self.sendMessage = sendMessage
self.classLabels = None
self.colors = None
self.nmsThreshold = 0.35
self.fontScale = float(fontScale)
self.fontThickness = 2
self.net = None
self.rgb = True
self.verbose = False
self.lastMessageSentTime = datetime.now()
# Read class names from text file
logging.info(' - Setting Classes')
with open(classesFile, 'r') as f:
self.classLabels = [line.strip() for line in f.readlines()]
# Generate colors for different classes
logging.info(' - Setting Colors')
self.colors = np.random.uniform(100, 255, size=(len(self.classLabels), 3))
# Read pre-trained model and config file
logging.info(' - Loading Model and Config')
darknet.performDetect( configPath = yolocfg, weightPath = yoloweight, metaPath= dataFile, initOnly= True )
def upload():
target = os.path.join(APP_ROOT, 'images/')
if not os.path.isdir(target):
os.mkdir(target)
for file in request.files.getlist("file"):
print(file)
filename = file.filename
destination = "/".join([target, filename])
file.save(destination)
result = darknet.performDetect(
imagePath='/home/dsbaule/PycharmProjects/Sketch2AIA/src/Web/images/'
+ filename,
thresh=0.25,
configPath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/NewDatasetYolov3.cfg",
weightPath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/NewDatasetYolov3_18000.weights",
metaPath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/obj.data",
showImage=True,
makeImageOnly=True,
initOnly=False)
destination = "/".join([target, filename[:-4] + 'Generated.jpg'])
import matplotlib
matplotlib.image.imsave(destination, result['image'])
return render_template("complete.html")
from darknet import darknet
from src.Component import Component
from src import Alignment
from src.AIA import AIAProject, GenerateAIA
result = darknet.performDetect(
imagePath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/TestImg/sample3.jpg",
thresh=0.25,
configPath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/NewDatasetYolov3.cfg",
weightPath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/NewDatasetYolov3_18000.weights",
metaPath=
"/home/dsbaule/PycharmProjects/Sketch2AIA/Custom_Darknet_Files/obj.data",
showImage=False,
makeImageOnly=False,
initOnly=False)
# for component in result:
# print(component[0] + ':')
# print('\t' + str(component[2][0]))
# print('\t' + str(component[2][1]))
# print('\t' + str(component[2][2]))
# print('\t' + str(component[2][3]))
componentList = list()
for component in result:
print(component[0])
if component[0] == 'Screen':