def _process_slide(self, slide: Slide):
slide_path = os.path.join(self.output_path, slide.name)
slide.slide_path = slide_path
os.mkdir(slide_path)
# Draw the slide image.
image = slide.image
image_path = os.path.join(slide_path, "image.png")
Logger.log_special("Scanning {}".format(slide.name), with_gap=True)
# Predict the cell masks from an image.
predict_image = image
if True:
equalizer_image = self.equalizer.create_equalized_image(image)
predict_image = equalizer_image
# Get the sample prediction.
prediction = self.net.cycle_predict(predict_image, None)
slide.cells = self._process_prediction(slide, slide_path, prediction)
self._draw_prediction_mask(image, slide_path, prediction)
pather.create("output/summary")
self.reporter.produce(slide, "output/summary")
cv2.imwrite(image_path, equalizer_image)
def display_stats(instances: Dict[str, int],
title: str = "SOMETHING",
file_name: str = "graph_name",
n_display: int = 20):
sorted_instances = sorted(instances.items(), key=lambda kv: kv[1])
sorted_instances.reverse()
Logger.log_special(title, with_gap=True)
for i in range(n_display):
Logger.log_field(
loader.get_label(sorted_instances[i][0]).upper(),
sorted_instances[i][1])
# Create the figure.
plt.style.use("ggplot")
fig, ax = plt.subplots(figsize=(15, 8))
short_instances = sorted_instances[:n_display]
# Labels.
y_label = [loader.get_label(s[0]) for s in short_instances]
y_label.insert(0, "(OTHERS)")
y = np.arange(len(y_label))
# Values.
x = [s[1] for s in short_instances]
x.insert(0, sum([s[1] for s in sorted_instances[n_display:]]))
c_map = plt.get_cmap("plasma")
colors = c_map(1 - y / len(y))
colors[0] = (0.7, 0.7, 0.7, 1.0)
# Plot the graph.
plt.barh(y, x, height=0.5, color=colors)
ax.set_yticks(y)
ax.set_yticklabels(y_label)
ax.invert_yaxis()
ax.set_title(f"{title}: ({len(samples)} Images)")
ax.set_xlabel("Count")
ax.set_ylabel("Class Name")
plt.savefig(f"{settings.OUTPUT_DIRECTORY}/{file_name}.png")
plt.clf()
__author__ = "Jakrin Juangbhanich"
__email__ = "*****@*****.**"
# body_labels = ['MAN', 'WOMAN', 'PERSON', 'GIRL', 'BOY']
body_labels = ['/m/04yx4', '/m/03bt1vf', '/m/01g317', '/m/05r655', '/m/01bl7v']
# face_labels = ['HUMAN FACE', 'HUMAN HEAD']
face_labels = ['/m/0dzct', '/m/04hgtk']
# car_labels = ['Land vehicle']
car_labels = ['/m/01prls']
if __name__ == "__main__":
# Load the project settings and required modules.
Logger.log_special("Running Sample Analysis", with_gap=True)
settings = ProjectSettings("settings.yaml")
# Load the class labels.
loader = Loader()
loader.load_labels(settings.LABELS_FILE)
# Get ALL of the samples in the directory.
samples = []
sample_files = os.listdir(settings.SAMPLES_DIRECTORY)
for i in sample_files[:20]:
file_path = os.path.join(settings.SAMPLES_DIRECTORY, i)
samples += Loader.load_sample_set_from_file(file_path)
class_instances = {}
class_appearances = {}
__author__ = "Jakrin Juangbhanich"
__email__ = "*****@*****.**"
# This is the maximum number of samples that a single 'set' will contain.
MAX_SAMPLE_SET_SIZE = 5000
# Remote URLs
REMOTE_IMAGE_URL_FILE = "https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train-images" \
"-boxable.csv "
REMOTE_GROUND_TRUTH_FILE = "https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train" \
"-annotations-bbox.csv "
if __name__ == "__main__":
# Load the project settings and required modules.
Logger.log_special("Running Sample Creator", with_gap=True)
settings = ProjectSettings("settings.yaml")
loader: Loader = Loader()
# Read in the source data, and create our own sample data.
Logger.log_special("Begin Sample Initialization", with_gap=True)
loader.check_and_load(settings.IMAGE_URL_FILE, REMOTE_IMAGE_URL_FILE)
samples = loader.create_samples(settings.IMAGE_URL_FILE)
# Now that we have sample IDs and URLs, we can associate them with the GT annotations.
Logger.log_special("Begin Sample Association", with_gap=True)
loader.check_and_load(settings.IMAGE_URL_FILE, REMOTE_GROUND_TRUTH_FILE)
loader.associate_boxes_with_samples(samples, settings.GROUND_TRUTH_FILE)
# Exporting the created samples.
Logger.log_special("Begin Sample Export", with_gap=True)
parser.add_argument("-n",
"--sample_count",
default=50,
type=int,
help="How many do we want to visualize?")
return parser.parse_args()
args = get_args()
set_index = args.set_index
sample_count = args.sample_count
if __name__ == "__main__":
# Load the project settings and required modules.
Logger.log_special("Running Sample Loader", with_gap=True)
settings = ProjectSettings("settings.yaml")
# Load the label mapping.
loader = Loader()
loader.load_labels(settings.LABELS_FILE)
Logger.log_field("Labels Loaded", len(loader.label_map))
# Load the samples from the set that we want.
samples = Loader.load_sample_set(set_index)
loaded_samples = [
s for s in samples
if (s.is_locally_loaded and len(s.detect_regions) > 0)
]
# How many samples loaded?
parser.add_argument("-m",
"--max_threads",
default=5,
type=int,
help="Max threads to use for loading.")
return parser.parse_args()
args = get_args()
set_index = args.set_index
max_threads = args.max_threads
if __name__ == "__main__":
# Load the project settings and required modules.
Logger.log_special("Running Sample Loader", with_gap=True)
settings = ProjectSettings("settings.yaml")
set_path = os.path.join(settings.SAMPLES_DIRECTORY,
f"sample_set_{set_index}.json")
if not os.path.exists(set_path):
Logger.log_field(
"Error",
"No file found at {}. Have you created the samples using cmd_create_samples yet?"
)
exit(1)
Logger.log_special("Begin Sample Image Download", with_gap=True)
samples = Loader.load_sample_set_from_file(set_path)
unloaded_samples = [s for s in samples if not s.is_locally_loaded]
n_unloaded_samples = len(unloaded_samples)