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Running
on
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Running
on
Zero
root
commited on
Commit
•
3f3b681
1
Parent(s):
9560d91
new demo button changed
Browse files- app.py +63 -28
- segment.py +4 -3
app.py
CHANGED
@@ -11,6 +11,7 @@ from pathlib import Path
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from PIL import Image
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from functools import partial
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from main import run_main
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LENGTH=512 #length of the square area displaying/editing images
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TRANSPARENCY = 150 # transparency of the mask in display
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@@ -53,7 +54,9 @@ def load_image_ui(load_edit, input_folder="example_tmp"):
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image = image.convert('RGB')
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segmentation = create_segmentation(mask_np_list)
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print("!!", len(mask_np_list))
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-
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except:
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print("Image folder invalid: The folder should contain image.png")
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return None, None, None, None, None
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@@ -172,6 +175,7 @@ def slider_release(index, image, mask_np_list_updated, mask_label_list):
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return new_image, mask_label
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def save_as_orig_mask(mask_np_list_updated, mask_label_list, input_folder="example_tmp"):
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try:
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assert np.all(sum(mask_np_list_updated)==1)
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except:
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@@ -186,6 +190,7 @@ def save_as_orig_mask(mask_np_list_updated, mask_label_list, input_folder="examp
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visualize_mask_list_clean(mask_np_list_updated, savepath)
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def save_as_edit_mask(mask_np_list_updated, mask_label_list, input_folder="example_tmp"):
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try:
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assert np.all(sum(mask_np_list_updated)==1)
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except:
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@@ -197,7 +202,22 @@ def save_as_edit_mask(mask_np_list_updated, mask_label_list, input_folder="examp
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savepath = os.path.join(input_folder, "seg_edited.png")
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visualize_mask_list_clean(mask_np_list_updated, savepath)
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-
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import shutil
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if os.path.isdir("./example_tmp"):
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shutil.rmtree("./example_tmp")
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@@ -224,35 +244,26 @@ with gr.Blocks() as demo:
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canvas = gr.Image(value = "./img.png", type="numpy", label="Draw Mask", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
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segment_button = gr.Button("1.1 Run segmentation")
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segment_button.click(run_segmentation,
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[canvas, block_flag] ,
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[block_flag] )
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text_button = gr.Button("Waiting 1.1 to complete")
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[image_loaded, segmentation, mask_np_list, mask_label_list, canvas] )
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load_edit_button = gr.Button("Waiting 1.1 to complete")
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load_edit_button.click(load_image_ui,
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[ true] ,
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[image_loaded, segmentation, mask_np_list, mask_label_list, canvas] )
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show_segment = gr.Checkbox(label = "Waiting 1.1 to complete")
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flag = gr.State(False)
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show_segment.select(show_segmentation,
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[image_loaded, segmentation, flag],
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[canvas, flag])
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def show_more_buttons():
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-
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block_flag.change(show_more_buttons, [], [text_button,load_edit_button,show_segment ])
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# mask_np_list_updated.value = copy.deepcopy(mask_np_list.value) #!!
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mask_np_list_updated = mask_np_list
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with gr.Column():
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gr.Markdown("""<p style="text-align: center; font-size: 20px">Edit Mask (Optional)</p>""")
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slider = gr.Slider(0, 20, step=1, interactive=
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label = gr.Textbox()
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slider.release(slider_release,
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inputs = [slider, image_loaded, mask_np_list_updated, mask_label_list],
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@@ -283,12 +294,25 @@ with gr.Blocks() as demo:
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add_mask_button.click(add_mask,
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[mask_np_list_updated, mask_label_list] ,
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[mask_np_list_updated, mask_label_list] )
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with gr.Tab(label="2 Optimization"):
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with gr.Row():
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with gr.Column():
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txt_box = gr.Textbox("Click to start optimization...", interactive = False)
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opt_flag = gr.State(0)
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gr.Markdown("""<p style="text-align: center; font-size: 20px">Optimization settings (SD)</p>""")
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@@ -312,7 +336,7 @@ with gr.Blocks() as demo:
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diffusion_model_learning_rate ,
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max_diffusion_train_steps,
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train_batch_size,
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gradient_accumulation_steps
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):
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run_optimization = partial(
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run_main,
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@@ -325,8 +349,16 @@ with gr.Blocks() as demo:
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gradient_accumulation_steps=int(gradient_accumulation_steps)
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)
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run_optimization()
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add_button.click(run_optimization_wrapper,
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inputs = [
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opt_flag,
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@@ -338,15 +370,18 @@ with gr.Blocks() as demo:
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train_batch_size,
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gradient_accumulation_steps
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],
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outputs = [
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)
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def change_text(txt_box):
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return gr.Textbox("Optimization Finished!", interactive = False)
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def change_text2(
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return gr.Textbox("Start optimization, check logs for progress...", interactive = False)
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add_button.click(change_text2,
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opt_flag.change(change_text, txt_box, txt_box)
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with gr.Tab(label="3 Editing"):
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with gr.Tab(label="3.1 Text-based editing"):
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@@ -401,7 +436,7 @@ with gr.Blocks() as demo:
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tgt_prompt ,
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tgt_index
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],
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outputs = [canvas_text_edit]
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)
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def load_pil_img():
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from PIL import Image
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from functools import partial
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from main import run_main
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import time
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LENGTH=512 #length of the square area displaying/editing images
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TRANSPARENCY = 150 # transparency of the mask in display
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image = image.convert('RGB')
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segmentation = create_segmentation(mask_np_list)
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print("!!", len(mask_np_list))
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max_val = len(mask_np_list)-1
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sliderup = gr.Slider.update(value = 0, minimum=0, maximum=max_val, step=1, interactive=True)
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return image, segmentation, mask_np_list, mask_label_list, image, sliderup
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except:
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print("Image folder invalid: The folder should contain image.png")
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return None, None, None, None, None
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return new_image, mask_label
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def save_as_orig_mask(mask_np_list_updated, mask_label_list, input_folder="example_tmp"):
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print(mask_np_list_updated)
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try:
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assert np.all(sum(mask_np_list_updated)==1)
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except:
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visualize_mask_list_clean(mask_np_list_updated, savepath)
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def save_as_edit_mask(mask_np_list_updated, mask_label_list, input_folder="example_tmp"):
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print(mask_np_list_updated)
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try:
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assert np.all(sum(mask_np_list_updated)==1)
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except:
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savepath = os.path.join(input_folder, "seg_edited.png")
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visualize_mask_list_clean(mask_np_list_updated, savepath)
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def image_change():
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directory_path = "./example_tmp/"
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for filename in os.listdir(directory_path):
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file_path = os.path.join(directory_path, filename)
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if os.path.isfile(file_path) or os.path.islink(file_path):
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os.unlink(file_path)
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elif os.path.isdir(file_path):
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shutil.rmtree(file_path)
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return gr.Button.update("1.2 Load original masks",visible = False), gr.Button.update("1.2 Load edited masks",visible = False), gr.Checkbox.update(label = "Show Segmentation",visible = False)
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def button_clickable(is_clickable):
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return gr.Button.update(interactive=is_clickable)
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import shutil
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if os.path.isdir("./example_tmp"):
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shutil.rmtree("./example_tmp")
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canvas = gr.Image(value = "./img.png", type="numpy", label="Draw Mask", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
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segment_button = gr.Button("1.1 Run segmentation")
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text_button = gr.Button("Waiting 1.1 to complete",visible = False)
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load_edit_button = gr.Button("Waiting 1.1 to complete",visible = False)
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show_segment = gr.Checkbox(label = "Waiting 1.1 to complete",visible = False)
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flag = gr.State(False)
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show_segment.select(show_segmentation,
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[image_loaded, segmentation, flag],
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[canvas, flag])
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#def show_more_buttons():
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# return gr.Button("1.2 Load original masks",visible = True), gr.Button("1.2 Load edited masks") , gr.Checkbox(label = "Show Segmentation")
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#block_flag.change(show_more_buttons, [], [text_button,load_edit_button,show_segment ])
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# mask_np_list_updated.value = copy.deepcopy(mask_np_list.value) #!!
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mask_np_list_updated = mask_np_list
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with gr.Column():
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gr.Markdown("""<p style="text-align: center; font-size: 20px">Edit Mask (Optional)</p>""")
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slider = gr.Slider(0, 20, step=1, interactive=False)
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label = gr.Textbox()
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slider.release(slider_release,
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inputs = [slider, image_loaded, mask_np_list_updated, mask_label_list],
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add_mask_button.click(add_mask,
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[mask_np_list_updated, mask_label_list] ,
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[mask_np_list_updated, mask_label_list] )
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segment_button.click(run_segmentation,
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[canvas] ,
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[text_button,load_edit_button,show_segment] )
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text_button.click(load_image_ui, [false] ,
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[image_loaded, segmentation, mask_np_list, mask_label_list, canvas, slider] )
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load_edit_button.click(load_image_ui, [ true] ,
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[image_loaded, segmentation, mask_np_list, mask_label_list, canvas, slider] )
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canvas.upload(image_change, inputs=[], outputs=[text_button,load_edit_button,show_segment])
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with gr.Tab(label="2 Optimization"):
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with gr.Row():
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with gr.Column():
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#txt_box = gr.Textbox("Click the button to start optimization...", interactive = False)
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opt_flag = gr.State(0)
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gr.Markdown("""<p style="text-align: center; font-size: 20px">Optimization settings (SD)</p>""")
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diffusion_model_learning_rate ,
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max_diffusion_train_steps,
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train_batch_size,
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gradient_accumulation_steps,
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):
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run_optimization = partial(
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run_main,
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gradient_accumulation_steps=int(gradient_accumulation_steps)
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)
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run_optimization()
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print('finish')
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#return gr.Button.update(value="Optimization finished!", interactive=False)
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def immediate_update():
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return gr.Button.update("Processing...", interactive=False)
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def immediate_update2():
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return gr.Button.update("Finished.", interactive=False)
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add_button.click(fn=immediate_update, inputs=[], outputs=[add_button])
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add_button.click(run_optimization_wrapper,
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inputs = [
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opt_flag,
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train_batch_size,
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gradient_accumulation_steps
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],
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outputs = [])
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add_button.click(fn=immediate_update2, inputs=[], outputs=[add_button])
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add_button.update()
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'''txt_box.change(fn=lambda x: gr.Button.update(value="Optimization Finished!", interactive=True),
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inputs=[txt_box], outputs=[add_button])
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def change_text(txt_box):
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return gr.Textbox("Optimization Finished!", interactive = False)
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def change_text2():
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return gr.Textbox("Start optimization, check logs for progress...", interactive = False)
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add_button.click(change_text2, [], txt_box)'''
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#opt_flag.change(change_text, txt_box, txt_box)
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with gr.Tab(label="3 Editing"):
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with gr.Tab(label="3.1 Text-based editing"):
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tgt_prompt ,
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tgt_index
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],
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outputs = [canvas_text_edit],queue=True,
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)
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def load_pil_img():
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segment.py
CHANGED
@@ -10,6 +10,7 @@ import os
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import numpy as np
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import argparse
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import matplotlib
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def load_image(image_path, left=0, right=0, top=0, bottom=0, size = 512):
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if type(image_path) is str:
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@@ -89,7 +90,7 @@ def draw_panoptic_segmentation(segmentation, segments_info,save_folder=None, nos
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def run_segmentation(image,
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base_folder_path = "."
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@@ -115,5 +116,5 @@ def run_segmentation(image, block_flag, name="example_tmp", size = 512, noseg=Fa
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os.makedirs(save_folder, exist_ok=True)
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draw_panoptic_segmentation(**panoptic_segmentation, save_folder = save_folder, noseg = noseg, model = model)
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print("Finish segment")
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block_flag += 1
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return
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import numpy as np
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import argparse
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import matplotlib
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import gradio as gr
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def load_image(image_path, left=0, right=0, top=0, bottom=0, size = 512):
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if type(image_path) is str:
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def run_segmentation(image, name="example_tmp", size = 512, noseg=False):
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base_folder_path = "."
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os.makedirs(save_folder, exist_ok=True)
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draw_panoptic_segmentation(**panoptic_segmentation, save_folder = save_folder, noseg = noseg, model = model)
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print("Finish segment")
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#block_flag += 1
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return gr.Button.update("1.2 Load original masks",visible = True), gr.Button.update("1.2 Load edited masks",visible = True), gr.Checkbox.update(label = "Show Segmentation",visible = True)
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