print "USAGE: python Accuracy cascade_xml_path test_annotation_dir" exit() try: #Open up the cascade classifier cascade = CascadeClassifier(cascade_xml_path) except: print "Error reading cascade xml" exit() #Get the file paths to read from detector_output_files = directory_paths(test_annotation_dir) #Open all the files and read the JSON annotation_files = batch_open_deserialise(detector_output_files) #For each of the hand marked test files for annotation_file in annotation_files: #Read the image corresponding to the annotation img = imread(fix_path(annotation_file['imagePath'])) print "detecting... " classifier_output = cascade.detectMultiScale(img, minNeighbors=3, minSize=(175, 175), #Constrain scale within a range around training scale maxSize=(225, 225)) #Run the detector total_detections = len(classifier_output) fp = 0 tp = 0
'''Get command line args'''
input_dir = sys.argv[1]
output_dir = sys.argv[2]
except:
print "USAGE: python GroundTruthMasks input_dir output_dir"
exit()
#Output fill colours for different types of annotations
annotation2fill = { 'golgi': 'white',
'ambiguous': 'blue'} #'white'}
#Get the file paths to read from
annotation_files = directory_paths(input_dir)
#Open all the files and read the JSON
files = batch_open_deserialise(annotation_files)
#For all the ground truths
for file in files:
polygons = file['shapes']
file['imagePath'] = fix_path(file['imagePath']) #Force the file ext. to .jpg. Sometimes it's stored wrong and needs this correction.
print "opening " + file['imagePath']
image = Image.open(file['imagePath'])
polygon_images = []
#For each ground truth annotation
for polygon in polygons:
polygon_label = polygon['label']
#Make the list of points into a Shapely Polygon object