Merge pull request #47 from andreped/liver-support

demo/src/compute.py

@@ -1,11 +1,17 @@
 import subprocess as sp
 
+from .logger import get_logger
+
+
+LOGGER = get_logger()
+
 
 def run_model(fixed_path=None, moving_path=None, fixed_seg_path=None, moving_seg_path=None, output_path=None, task="B"):
     if (fixed_seg_path is None) or (moving_seg_path is None):
-        print("The fixed or moving segmentation were not provided and are thus ignored for inference.")
+        LOGGER.info("The fixed or moving segmentation were not provided and are thus ignored for inference.")
         sp.check_call(["ddmr", "-f", fixed_path, "-m", moving_path, \
                     "-o", output_path, "-a", task, "--model", "BL-NS", "--original-resolution"])
     else:
+        LOGGER.info("All four inputs were successfully provided.")
         sp.check_call(["ddmr", "-f", fixed_path, "-m", moving_path, "-fs", fixed_seg_path, "-ms", moving_seg_path,
                     "-o", output_path, "-a", task, "--model", "BL-NS", "--original-resolution"])

demo/src/gui.py

@@ -1,9 +1,14 @@
 import gradio as gr
 
 from .compute import run_model
+from .logger import setup_logger, read_logs
 from .utils import load_ct_to_numpy
 
 
+# setup logging
+LOGGER = setup_logger()
+
+
 class WebUI:
     def __init__(
         self,
@@ -27,6 +32,7 @@ class WebUI:
             "Brain": "B",
             "Liver": "L"
         }
+        self.class_name = "Brain"
 
         self.fixed_image_path = None
         self.moving_image_path = None
@@ -42,12 +48,12 @@ class WebUI:
             label="Which 2D slice to show",
         )
 
-        self.run_btn = gr.Button("Run analysis").style(
+        self.run_btn = gr.Button("Run analysis", show_progress="full", elem_id="button").style(
             full_width=False, size="lg"
         )
 
     def set_class_name(self, value):
-        print("Changed task to:", value)
+        LOGGER.info(f"Changed task to: {value}")
         self.class_name = value
 
     def upload_file(self, files):
@@ -85,7 +91,7 @@ class WebUI:
         self.moving_images = load_ct_to_numpy(self.moving_image_path)
         self.pred_images = load_ct_to_numpy(output_path + "pred_image.nii.gz")
 
-        return None
+        return self.pred_images[0]
 
     def get_fixed_image(self, k):
         k = int(k) - 1
@@ -121,7 +127,13 @@ class WebUI:
         margin: auto;
         }
         #upload {
-        height: 80px;
+        height: 120px;
+        }
+        #button {
+        height: 120px;
+        }
+        #dropdown {
+        height: 120px;
         }
         """
         with gr.Blocks(css=css) as demo:
@@ -149,6 +161,9 @@ class WebUI:
                         info="Which task to perform image-to-registration on",
                         multiselect=False,
                         size="sm",
+                        default="Brain",
+                        elem_id="dropdown",
+
                     )
                     model_selector.input(
                         fn=lambda x: self.set_class_name(x),
@@ -158,19 +173,8 @@ class WebUI:
 
                     self.run_btn.render()
 
-            """
-            with gr.Row():
-                gr.Examples(
-                    examples=[
-                        os.path.join(self.cwd, "ixi_image.nii.gz"),
-                        os.path.join(self.cwd, "ixi_image2.nii.gz"),
-                    ],
-                    inputs=file_output,
-                    outputs=file_output,
-                    fn=self.upload_file,
-                    cache_examples=True,
-                )
-            """
+                logs = gr.Textbox(label="Logs", info="Verbose from inference will be displayed below.", max_lines=8, autoscroll=True)
+                demo.load(read_logs, None, logs, every=1)
 
             with gr.Row():
                 with gr.Box():
@@ -201,6 +205,8 @@ class WebUI:
                             
                             pred_images = []
                             for i in range(self.nb_slider_items):
+                                if i == 0:
+                                    first_pred_component = t
                                 visibility = True if i == 1 else False
                                 t = gr.Image(
                                     visible=visibility, elem_id="model-2d", label="predicted fixed image", show_label=True,
@@ -213,7 +219,7 @@ class WebUI:
                             self.run_btn.click(
                                 fn=self.process,
                                 inputs=None,
-                                outputs=None,
+                                outputs=first_pred_component,
                             )
 
                         self.slider.input(

demo/src/logger.py

@@ -0,0 +1,30 @@
+import logging
+import sys
+
+
+def get_logger():
+    logging.getLogger(__name__)
+
+
+def setup_logger():
+    # clear log
+    file_to_delete = open("log.log",'w')
+    file_to_delete.close()
+
+    file_handler = logging.FileHandler(filename='log.log')
+    stdout_handler = logging.StreamHandler(stream=sys.stdout)
+    handlers = [file_handler, stdout_handler]
+
+    logging.basicConfig(
+        level=logging.INFO, 
+        format='[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
+        handlers=handlers,
+    )
+
+    return get_logger()
+
+
+def read_logs():
+    sys.stdout.flush()
+    with open("log.log", "r") as f:
+        return f.read()