bugfix

src/app.py

@@ -2,72 +2,20 @@ import os
 import streamlit as st
 import albumentations as A
 
-from utils import load_augmentations_config, get_arguments
+from utils import (
+    load_augmentations_config,
+    get_arguments,
+    get_placeholder_params,
+    select_transformations,
+    show_random_params,
+)
 from visuals import (
-    show_transform_control,
     select_image,
     show_credentials,
     show_docstring,
+    get_transormations_params,
 )
 
-
-def get_placeholder_params(image):
-    return {
-        "image_width": image.shape[1],
-        "image_height": image.shape[0],
-        "image_half_width": int(image.shape[1] / 2),
-        "image_half_height": int(image.shape[0] / 2),
-    }
-
-
-def select_transformations(augmentations: dict, interface_type: str) -> list:
-    # in the Simple mode you can choose only one transform
-    if interface_type == "Simple":
-        transform_names = [
-            st.sidebar.selectbox(
-                "Select a transformation:", sorted(list(augmentations.keys()))
-            )
-        ]
-    # in the professional mode you can choose several transforms
-    elif interface_type == "Professional":
-        transform_names = [
-            st.sidebar.selectbox(
-                "Select transformation №1:", sorted(list(augmentations.keys()))
-            )
-        ]
-        while transform_names[-1] != "None":
-            transform_names.append(
-                st.sidebar.selectbox(
-                    f"Select transformation №{len(transform_names) + 1}:",
-                    ["None"] + sorted(list(augmentations.keys())),
-                )
-            )
-        transform_names = transform_names[:-1]
-    return transform_names
-
-
-def get_transormations_params(transform_names: list) -> list:
-    transforms = []
-    for i, transform_name in enumerate(transform_names):
-        # select the params values
-        st.sidebar.subheader("Params of the " + transform_name)
-        param_values = show_transform_control(augmentations[transform_name], i)
-        transforms.append(getattr(A, transform_name)(**param_values))
-    return transforms
-
-
-def show_random_params(data: dict, interface_type: str = "Professional"):
-    """Shows random params used for transformation (from A.ReplayCompose)"""
-    if interface_type == "Professional":
-        st.subheader("Random params used")
-        random_values = {}
-        for applied_params in data["replay"]["transforms"]:
-            random_values[
-                applied_params["__class_fullname__"].split(".")[-1]
-            ] = applied_params["params"]
-        st.write(random_values)
-
-
 # TODO: refactor all the new code
 
 # get CLI params: the path to images and image width
@@ -100,7 +48,7 @@ else:
         transform_names = select_transformations(augmentations, interface_type)
 
         # get parameters for each transform
-        transforms = get_transormations_params(transform_names)
+        transforms = get_transormations_params(transform_names, augmentations)
 
         try:
             # apply the transformation to the image

src/utils.py

@@ -109,3 +109,50 @@ def get_params_string(param_values: dict) -> str:
         [k + "=" + str(param_values[k]) for k in param_values.keys()]
     )
     return params_string
+
+
+def get_placeholder_params(image):
+    return {
+        "image_width": image.shape[1],
+        "image_height": image.shape[0],
+        "image_half_width": int(image.shape[1] / 2),
+        "image_half_height": int(image.shape[0] / 2),
+    }
+
+
+def select_transformations(augmentations: dict, interface_type: str) -> list:
+    # in the Simple mode you can choose only one transform
+    if interface_type == "Simple":
+        transform_names = [
+            st.sidebar.selectbox(
+                "Select a transformation:", sorted(list(augmentations.keys()))
+            )
+        ]
+    # in the professional mode you can choose several transforms
+    elif interface_type == "Professional":
+        transform_names = [
+            st.sidebar.selectbox(
+                "Select transformation №1:", sorted(list(augmentations.keys()))
+            )
+        ]
+        while transform_names[-1] != "None":
+            transform_names.append(
+                st.sidebar.selectbox(
+                    f"Select transformation №{len(transform_names) + 1}:",
+                    ["None"] + sorted(list(augmentations.keys())),
+                )
+            )
+        transform_names = transform_names[:-1]
+    return transform_names
+
+
+def show_random_params(data: dict, interface_type: str = "Professional"):
+    """Shows random params used for transformation (from A.ReplayCompose)"""
+    if interface_type == "Professional":
+        st.subheader("Random params used")
+        random_values = {}
+        for applied_params in data["replay"]["transforms"]:
+            random_values[
+                applied_params["__class_fullname__"].split(".")[-1]
+            ] = applied_params["params"]
+        st.write(random_values)

src/visuals.py

@@ -1,6 +1,8 @@
 import cv2
 import streamlit as st
 
+import albumentations as A
+
 from control import param2func
 from utils import get_images_list, load_image, upload_image
 
@@ -35,7 +37,7 @@ def select_image(path_to_images: str, interface_type: str = "Simple"):
         if image_name != "Upload my image":
             try:
                 image = load_image(image_name, path_to_images)
-                return 1, image
+                return 0, image
             except cv2.error:
                 return 1, 0
         else:
@@ -89,6 +91,16 @@ def show_credentials():
     )
 
 
+def get_transormations_params(transform_names: list, augmentations: dict) -> list:
+    transforms = []
+    for i, transform_name in enumerate(transform_names):
+        # select the params values
+        st.sidebar.subheader("Params of the " + transform_name)
+        param_values = show_transform_control(augmentations[transform_name], i)
+        transforms.append(getattr(A, transform_name)(**param_values))
+    return transforms
+
+
 def show_docstring(obj_with_ds):
     st.markdown("* * *")
     st.subheader("Docstring for " + obj_with_ds.__class__.__name__)