import os
from matplotlib import pyplot as plt
if __name__ == '__main__':
full_path = utils.get_path(in_or_out=utils.IN,
data_fold=utils.TRAINING,
full_dataset=True)
xml_files = [f for f in os.listdir(full_path) if f.endswith('.xml')]
roof_types = set()
roof_polygons = dict()
total_metal = 0
total_thatch = 0
total_tiled = 0
for xml_file in xml_files[:10]:
img_name = xml_file[:-4]
roof_polygons[img_name] = DataLoader.get_all_roofs_full_dataset(
merge_tiled=True, xml_name=xml_file, xml_path=full_path)
roof_types.update(roof_polygons[img_name].keys())
try:
print full_path + img_name + '.jpg'
image = cv2.imread(full_path + img_name + '.jpg')
except IOError as e:
print e
sys.exit()
for r, roof_type in enumerate(roof_polygons[img_name].keys()):
if roof_type == 'thatch':
color = (255, 0, 0)
total_thatch += len(roof_polygons[img_name][roof_type])
elif roof_type == 'metal':
color = (0, 255, 0)
def __init__(self, report_name=None, method=None,
full_dataset=True,
folder_name=None, save_imgs=True, out_path=None,
detections=None, in_path=None,
detector_names=None,
mergeFalsePos=False,
separateDetections=True,
vocGood=0.1, negThres = 0.3, auc_threshold=0.5, correct_roofs=None, img_names=None):
'''
Will score the detections class it contains.
Parameters:
--------------------
separateDetections: bool
Whether the metal detections can count as true positives for the thatch
detections and viceversa
mergeFalsePos: bool
Whether we need to keep track of the good and bad detections of metal
and thatch separately or not. We cannot separate them if we want to
train a single neural network to distinguish between both types of roofs
since the bad detections of either roof must not contain a positive detection
of the other type of roof (it should be background)
'''
self.TOTAL = 0
self.save_imgs = save_imgs
#these two are related to saving the FP and TP for neural training
self.mergeFalsePos=mergeFalsePos
self.auc_threshold = auc_threshold
self.keep_detections_separate = separateDetections
#threholds to classify detections as False/True positives and Good/Bad detections(these are to train the neural network on it)
self.VOC_threshold = utils.VOC_threshold #threshold to assign a detection as a true positive
self.VOC_good_detection_threshold = dict()
self.VOC_good_detection_threshold['metal'] = utils.VOC_threshold
self.VOC_good_detection_threshold['thatch'] = utils.VOC_threshold
self.detection_portion_threshold = 0.50
self.negThres = negThres
self.detections = detections
self.in_path = in_path #the path from which the images are taken
if img_names is None:
self.img_names = [f for f in listdir(self.in_path) if f.endswith('.jpg')]
else:
self.img_names = img_names
#the ground truth roofs for every image and roof type
if correct_roofs is None:
self.correct_roofs = dict()
for roof_type in utils.ROOF_TYPES:
self.correct_roofs[roof_type] = dict()
self.full_dataset = full_dataset
if full_dataset == False:
for img_name in self.img_names:
for roof_type in utils.ROOF_TYPES:
#we receive polygons, so we convert them into boxes so we can use the fast scoring
temp_roofs = DataLoader.get_polygons(roof_type=roof_type,
xml_name=img_name[:-3]+'xml' , xml_path=self.in_path)
if len(temp_roofs)>0:
self.correct_roofs[roof_type][img_name] = utils.polygons2boxes(temp_roofs)
else:
self.correct_roofs[roof_type][img_name] = []
self.detections.update_roof_num(self.correct_roofs[roof_type][img_name], roof_type)
else:
for img_name in self.img_names:
current_roofs = DataLoader.get_all_roofs_full_dataset(xml_name=img_name[:-3]+'xml' , xml_path=self.in_path)
for roof_type in utils.ROOF_TYPES:
if roof_type not in current_roofs:
self.correct_roofs[roof_type][img_name] = []
else:
self.correct_roofs[roof_type][img_name] = current_roofs[roof_type]
self.detections.update_roof_num(self.correct_roofs[roof_type][img_name], roof_type)
else:
self.correct_roofs = correct_roofs
#init the report file
self.out_path = out_path
if report_name is not None:
if detector_names is not None:
self.init_report(detector_names, report_name=report_name)
#variables needed to pickle the FP and TP to a file that makes sense
assert method is not None
self.method = method
self.folder_name = folder_name
def __init__(self,
report_name=None,
method=None,
full_dataset=True,
folder_name=None,
save_imgs=True,
out_path=None,
detections=None,
in_path=None,
detector_names=None,
mergeFalsePos=False,
separateDetections=True,
vocGood=0.1,
negThres=0.3,
auc_threshold=0.5,
correct_roofs=None,
img_names=None):
'''
Will score the detections class it contains.
Parameters:
--------------------
separateDetections: bool
Whether the metal detections can count as true positives for the thatch
detections and viceversa
mergeFalsePos: bool
Whether we need to keep track of the good and bad detections of metal
and thatch separately or not. We cannot separate them if we want to
train a single neural network to distinguish between both types of roofs
since the bad detections of either roof must not contain a positive detection
of the other type of roof (it should be background)
'''
self.TOTAL = 0
self.save_imgs = save_imgs
#these two are related to saving the FP and TP for neural training
self.mergeFalsePos = mergeFalsePos
self.auc_threshold = auc_threshold
self.keep_detections_separate = separateDetections
#threholds to classify detections as False/True positives and Good/Bad detections(these are to train the neural network on it)
self.VOC_threshold = utils.VOC_threshold #threshold to assign a detection as a true positive
self.VOC_good_detection_threshold = dict()
self.VOC_good_detection_threshold['metal'] = utils.VOC_threshold
self.VOC_good_detection_threshold['thatch'] = utils.VOC_threshold
self.detection_portion_threshold = 0.50
self.negThres = negThres
self.detections = detections
self.in_path = in_path #the path from which the images are taken
if img_names is None:
self.img_names = [
f for f in listdir(self.in_path) if f.endswith('.jpg')
]
else:
self.img_names = img_names
#the ground truth roofs for every image and roof type
if correct_roofs is None:
self.correct_roofs = dict()
for roof_type in utils.ROOF_TYPES:
self.correct_roofs[roof_type] = dict()
self.full_dataset = full_dataset
if full_dataset == False:
for img_name in self.img_names:
for roof_type in utils.ROOF_TYPES:
#we receive polygons, so we convert them into boxes so we can use the fast scoring
temp_roofs = DataLoader.get_polygons(
roof_type=roof_type,
xml_name=img_name[:-3] + 'xml',
xml_path=self.in_path)
if len(temp_roofs) > 0:
self.correct_roofs[roof_type][
img_name] = utils.polygons2boxes(temp_roofs)
else:
self.correct_roofs[roof_type][img_name] = []
self.detections.update_roof_num(
self.correct_roofs[roof_type][img_name], roof_type)
else:
for img_name in self.img_names:
current_roofs = DataLoader.get_all_roofs_full_dataset(
xml_name=img_name[:-3] + 'xml', xml_path=self.in_path)
for roof_type in utils.ROOF_TYPES:
if roof_type not in current_roofs:
self.correct_roofs[roof_type][img_name] = []
else:
self.correct_roofs[roof_type][
img_name] = current_roofs[roof_type]
self.detections.update_roof_num(
self.correct_roofs[roof_type][img_name], roof_type)
else:
self.correct_roofs = correct_roofs
#init the report file
self.out_path = out_path
if report_name is not None:
if detector_names is not None:
self.init_report(detector_names, report_name=report_name)
#variables needed to pickle the FP and TP to a file that makes sense
assert method is not None
self.method = method
self.folder_name = folder_name