Merge branch 'main' of https://github.com/MaxReimann/WISE-Editing into main

README.md

@@ -40,3 +40,14 @@ Then run the streamlit app using `streamlit run Whitebox_style_transfer.py`
 ### Further notes
 Pull Requests and further improvements welcome.
 Please note that the shown effect is a minimal pipeline in terms of stylization capability, the much more feature-rich oilpaint and watercolor pipelines we show in our ECCV paper cannot be open-sourced due to IP reasons.
+
+``` latex
+@misc{loetzsch2022wise,
+    title={WISE: Whitebox Image Stylization by Example-based Learning},
+    author={Lötzsch, Winfried and Reimann, Max and Büssemeyer, Martin and Semmo, Amir and Döllner, Jürgen and Trapp, Matthias},
+    year={2022},
+    eprint={2207.14606},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```

Whitebox_style_transfer.py

@@ -41,6 +41,9 @@ if "click_counter" not in st.session_state:
 
 if "action" not in st.session_state:
     st.session_state["action"] = ""
+    
+if "user" not in st.session_state:
+    st.session_state["user"] = hash(time.time())
 
 content_urls = [
     {
@@ -129,7 +132,7 @@ def img_choice_panel(imgtype, urls, default_choice, expanded):
                 st.write("uploaded.")
 
         last_clicked = last_image_clicked(type=imgtype)
-        print("last_clicked", last_clicked, "clicked", clicked, "action", st.session_state["action"] )
+        print(st.session_state["user"], " last_clicked", last_clicked, "clicked", clicked, "action", st.session_state["action"] )
         if not upload_pressed and clicked != "":  # trigger when no file uploaded
             if last_clicked != clicked:  # only activate when content was actually clicked
                 store_img_from_id(clicked, urls, imgtype)
@@ -146,6 +149,7 @@ def optimize(effect, preset, result_image_placeholder):
     style = st.session_state["Style_im"]
     st.session_state["optimize_next"] = False
     with st.spinner(text="Optimizing parameters.."):
+        print("optimizing for user", st.session_state["user"])
         if HUGGING_FACE:
             optimize_on_server(content, style, result_image_placeholder)
         else:
@@ -245,11 +249,11 @@ if st.session_state["action"] == "uploaded":
     content_img, _vp = optimize_next(result_image_placeholder)
 elif st.session_state["action"] in ("switch_page_from_local_edits", "switch_page_from_presets", "slider_change") or \
       content_id == "uploaded" or style_id == "uploaded":
-    print("restore param")
+    print(st.session_state["user"], "restore param")
     _vp = st.session_state["result_vp"]
     content_img = st.session_state["effect_input"]
 else:
-    print("load_params")
+    print(st.session_state["user"], "load_params")
     content_img, _vp = load_params(content_id, style_id)#, effect)
 
 vp = torch.clone(_vp)
@@ -284,7 +288,7 @@ with coll2:
 
     others_idx = set(range(len(effect.vpd.vp_ranges))) - set([effect.vpd.name2idx[name] for name in sum(params_mapping.values(), [])])
     others_names = [effect.vpd.vp_ranges[i][0] for i in sorted(list(others_idx))]
-    other_param = st.selectbox("Other parameters: ", others_names)
+    other_param = st.selectbox("Other parameters: ", ["hueShift"] + [n for n in others_names if n != "hueShift"] )
     create_slider(other_param)
 
 

pages/1_🎨_Apply_preset.py

@@ -116,8 +116,10 @@ with torch.no_grad():
 img_res = Image.fromarray((torch_to_np(result_cuda) * 255.0).astype(np.uint8))
 coll2.image(img_res)
 
+print(st.session_state["user"], " edited preset")
+
 apply_btn = st.sidebar.button("Apply")
 if apply_btn:
     st.session_state["result_vp"] = vp
 
-st.info("Note: Press apply to make changes permanent")
+st.info("Note: Press apply to make changes permanent")

pages/2_🖌️_Local_edits.py

@@ -222,6 +222,8 @@ if st.session_state.local_edit_action in ("slider", "param_change", "init"):
     print("set redraw")
     st.session_state.local_edit_action = "redraw"
 
+if "objects" in canvas_result.json_data and canvas_result.json_data["objects"] != []:
+    print(st.session_state["user"], " edited local param canvas")       
 
 print("plot masks")
 texts = []

pages/4_📖_Readme.py

@@ -2,7 +2,7 @@ import streamlit as st
 
 st.title("White-box Style Transfer Editing")
 
-print("readme opened")
+print(st.session_state["user"], " opened readme")
 st.markdown("""
     This app demonstrates the editing capabilities of the White-box Style Transfer Editing (WISE) framework.
     It optimizes the parameters of classical image processing filters to match a given style image.
@@ -21,9 +21,9 @@ st.markdown("""
     - Strokes stay on the canvas unless manually deleted by clicking the trash button. To remove them from the canvas after each stroke, tick the corresponding checkbox in the sidebar.
 
     ### Links & Paper 
-    [Project page](https://ivpg.hpi3d.de/wise/),
-    [arxiv link](https://arxiv.org/abs/2207.14606)
-    [demo code](https://github.com/MaxReimann/WISE-Editing)
+    **[Project page](https://ivpg.hpi3d.de/wise/),
+    [arxiv link](https://arxiv.org/abs/2207.14606),
+    [demo code](https://github.com/MaxReimann/WISE-Editing)**
 
     "WISE: Whitebox Image Stylization by Example-based Learning", by Winfried Lötzsch*, Max Reimann*, Martin Büßemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp, in ECCV 2022
 

tasks.py

@@ -108,7 +108,7 @@ def optimize_on_server(content, style, result_image_placeholder):
     style = pil_resize_long_edge_to(style, 1024)
     style.save(style_path)
     files = {'style-image': open(style_path, "rb"), "content-image": open(content_path, "rb")}
-    print("start-optimizing")
+    print("start-optimizing. Time: ", datetime.datetime.now())
     url = WORKER_URL + "/upload"
     task_id_res = requests.post(url, files=files)
     if task_id_res.status_code != 200:
@@ -140,4 +140,4 @@ def optimize_params(effect, preset, content, style, result_image_placeholder):
                                         write_video=False, base_dir=base_dir,
                                         iter_callback=lambda i: progress_bar.progress(
                                             float(i) / ST_CONFIG["n_iterations"]))
-    st.session_state["effect_input"], st.session_state["result_vp"]  = content_img_cuda.detach(), vp.cuda().detach()
+    st.session_state["effect_input"], st.session_state["result_vp"]  = content_img_cuda.detach(), vp.cuda().detach()