def update_output_rf(slide_path):
uid = path_leaf(slide_path).replace(".tif", "")
pickle_file = os.path.join(PREDICTED_HEATMAP_DIR["rf"],
uid + ".joblib.pickle")
slide = WSIReader(slide_path, 40)
n_cols = int(slide.width / 256)
n_rows = int(slide.height / 256)
thumbnail = slide.get_thumbnail((n_cols, n_rows))
thumbnail = np.array(thumbnail)
if os.path.isfile(pickle_file):
thumbnail_predicted = joblib.load(pickle_file)
fig, ax = plt.subplots()
plot_blend(thumbnail, thumbnail_predicted, ax, alpha=1)
# ax.axis('off')
ax.set_title("RF - Predicted Heatmap")
else:
fig, ax = plt.subplots()
thumbnail = imread(NOT_AVAIL_IMG)
ax.imshow(thumbnail)
ax.axis("off")
# ax.axis('off')
fig.tight_layout()
slide.close()
out_url = fig_to_uri(fig)
plt.close("all")
return out_url
def get_samples_from_dir(dir):
# Just assume all files will have tif extension
wsis = glob.glob(os.path.join(dir, "*.tif"))
short_names = []
for wsi in wsis:
label = path_leaf(wsi).replace(".tif", "")
# if label in TUMOR_VALIDATE_SLIDES or label in NORMAL_VALIDATE_SLIDES:
short_names.append({"label": label, "value": wsi})
return short_names
def update_output_rf_mask(slide_path):
uid = path_leaf(slide_path).replace(".tif", "")
pickle_file = os.path.join(PREDICTED_HEATMAP_DIR["rf"],
uid + ".joblib.pickle")
if os.path.isfile(pickle_file):
thumbnail_predicted = joblib.load(pickle_file)
fig, ax = plt.subplots()
ax.imshow((thumbnail_predicted > 0.75).astype(np.int),
cmap="gray",
vmin=0,
vmax=1)
# ax.set_title(' (white=tumor, black=not_tumor)')
# ax.axis('off')
ax.set_title("RF - Predicted Mask \n (white=tumor, black=normal)")
else:
fig, ax = plt.subplots()
thumbnail = imread(NOT_AVAIL_IMG)
ax.imshow(thumbnail)
ax.axis("off")
fig.tight_layout()
out_url = fig_to_uri(fig)
plt.close("all")
return out_url