def __init__(self, original_path, result_path):
super(MyWindow, self).__init__()
uic.loadUi('eval_gui.ui', self)
self.show()
self.setWindowTitle("Evaluation")
self.answers = []
# Load the list of images and shuffle them
# Note: Make sure images match in each folder
# i.e. they are in the same order in each dir
paths_zipped = list(
zip(files.get_images(original_path),
files.get_images(result_path)))
shuffle(paths_zipped) # shuffle the list of (original, result) paths
original_paths, result_paths = zip(
*paths_zipped) # Unzip into two different lists
# Check if the number of images is the same
if len(original_paths) != len(result_paths):
print("Different num of images")
sys.exit(0)
# Dict to store each set of images
self.img_paths = {"original": original_paths, "result": result_paths}
# Make paths dynamic from CLI args
self.fileNum = len(self.img_paths["original"])
self.fileCount = 0
print('Number of files: {}'.format(self.fileNum))
# Binding the buttons to functions
self.correctButton.clicked.connect(lambda: self.correct_image())
self.nearButton.clicked.connect(lambda: self.near_image())
self.incorrectButton.clicked.connect(lambda: self.incorrect_image())
# Create the scenes for the 2D graphics viewers
self.scenes = {
"original": QtWidgets.QGraphicsScene(),
"result": QtWidgets.QGraphicsScene()
}
# Bind the scenes to the correct objs
self.original.setScene(self.scenes['original'])
self.preprocessed.setScene(self.scenes['result'])
# For each scene, draw the first image at th start
for s_name, s_obj in self.scenes.items():
self.__set_scene_image(s_name, self.img_paths[s_name][0])
# Timing user input
self.timeStart = time.process_time()
print("Root directory........ %s" % root_path)
print("Output directory...... %s\n" % out_path)
# Check that both directories exist
if os.path.lexists(out_path) and os.path.lexists(root_path):
pass
else:
print(Fore.RED +
"Path not found, one of the directories does not exist.")
print(Style.RESET_ALL)
sys.exit(0) # Use system's abort function
# Reset colour
print(Style.RESET_ALL)
images = files.get_images(root_path) # Note line below resets the blue
print(Style.RESET_ALL + Fore.GREEN +
"Number of images in the path: {:d}".format(len(images)))
# Create the image matrix here
height, width = 30, 30
img_matrix = np.zeros((len(images), height * width)) # (num_imgs x dim)
# Going through all the images and polutating the img_matrix
size = 20 # Length of the loading bar in chars
for i, img_path in enumerate(images):
# Open each image
img = ut.read_image(img_path, "BGR2GRAY")
resized = cv2.resize(img, (height, width), cv2.INTER_AREA)
# Reset colour
print(Style.RESET_ALL)
# Gather the data
data = gather_data(eval_path)
responses = gather_responses(data)
response_times = gather_response_times(data)
##########################################
## Extracting the response images stuff ##
#########################################
# Get all the images in the dataset
image_paths = files.get_images(input_path)
# Get the list of JUST the file names from all the images
dataset_names = list(map(files.get_filename, image_paths))
# Get the response file names
response_names = get_responsefiles(1, data)
print("response is: {}".format(response_names))
# Make sure output path exists
if not os.path.exists(output_path):
os.mkdir(output_path) #mode = 777
# Loop to go trhough the response_times and copy the
# images to the output directory
for name in response_names: