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import gradio as gr
import cv2
import numpy as np
from PIL import Image
examples = [
'176788.jpg',
'182862.jpg'
]
masks = [
'mask1.jpg',
'mask2.jpg'
]
def edge_detection(mode_selector, upload_input, image_selector, image_selected, threshold1, threshold2):
if mode_selector == "Upload":
image = upload_input
else:
image = image_selected
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, threshold1, threshold2)
return edges
def mode_selector_changed(mode_selector, image_selector):
if mode_selector == "Upload":
return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=True), gr.update(visible=image_selector is not None, value=image_selector)
def mode_selector_changed_inpainting(mode_selector, image_selector, mask_selector):
if mode_selector == "Upload":
return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=image_selector is not None, value=image_selector), gr.update(visible=True), gr.update(visible=image_selector is not None, value=mask_selector)
def close_selected_image(image_selected):
if image_selected is None:
return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
else:
return gr.update(visible=True), gr.update(visible=True)
def close_selected_mask(mask_selected):
if mask_selected is None:
return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
else:
return gr.update(visible=True), gr.update(visible=True)
def image_segmentation(mode_selector, upload_input, image_selector, image_selected):
if mode_selector == "Upload":
image = upload_input
else:
image = image_selected
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, segmented = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
return segmented
def image_inpainting(mode_selector, upload_input, upload_mask, image_selector, image_selected, mask_selector, mask_selected):
if mode_selector == "Upload":
image = upload_input
mask = upload_mask
else:
image = image_selected
mask = mask_selected
if mask.shape[:2] != image.shape[:2]:
mask = cv2.resize(mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
if len(mask.shape) == 3:
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
mask = (mask > 0).astype(np.uint8) * 255
inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
return inpainted
with gr.Blocks() as demo:
with gr.Tab("Edge Detection"):
mode_selector = gr.Radio(["Upload", "Example"], value="Upload", label="Upload an image or using example image")
# Mode 1
upload_input = gr.Image(type="numpy", label="Upload Image", visible=True)
# Mode 2
image_selector = gr.Radio(examples, label="Select an example image", visible=False)
image_selected = gr.Image(None, label="Selected Image", visible=False, sources=[])
threshold1 = gr.Slider(0, 255, value=50, step=1, label="Threshold 1")
threshold2 = gr.Slider(0, 255, value=100, step=1, label="Threshold 2")
output = gr.Image(type="numpy", label="Edge Image")
inputs = [
mode_selector,
upload_input,
image_selector,
image_selected,
threshold1,
threshold2
]
mode_selector.change(
mode_selector_changed,
inputs=[mode_selector, image_selector],
outputs=[upload_input, image_selector, image_selected]
)
image_selector.change(
mode_selector_changed,
inputs=[mode_selector, image_selector],
outputs=[upload_input, image_selector, image_selected]
)
image_selected.change(
close_selected_image,
inputs=[image_selected],
outputs=[image_selector, image_selected]
)
gr.Interface(
fn=edge_detection,
inputs=inputs,
outputs=output,
description="Upload an image and adjust the thresholds to perform edge detection",
flagging_mode='never'
)
with gr.Tab("Image Segmentation"):
mode_selector = gr.Radio(["Upload", "Example"], value="Upload", label="Upload an image or using example image")
# Mode 1
upload_input = gr.Image(type="numpy", label="Upload Image", visible=True)
# Mode 2
image_selector = gr.Radio(examples, label="Select an example image", visible=False)
image_selected = gr.Image(None, label="Selected Image", visible=False, sources=[])
output = gr.Image(type="numpy", label="Edge Image")
inputs = [
mode_selector,
upload_input,
image_selector,
image_selected,
]
mode_selector.change(
mode_selector_changed,
inputs=[mode_selector, image_selector],
outputs=[upload_input, image_selector, image_selected]
)
image_selector.change(
mode_selector_changed,
inputs=[mode_selector, image_selector],
outputs=[upload_input, image_selector, image_selected]
)
image_selected.change(
close_selected_image,
inputs=[image_selected],
outputs=[image_selector, image_selected]
)
gr.Interface(
fn=image_segmentation,
inputs=inputs,
outputs=output,
description="Upload an image and adjust the thresholds to perform edge detection",
flagging_mode='never'
)
with gr.Tab("Image Inpainting"):
mode_selector = gr.Radio(["Upload", "Example"], value="Upload", label="Upload an image or using example image")
# Mode 1
upload_input = gr.Image(type="numpy", label="Upload Image", visible=True)
upload_mask = gr.Image(type="numpy", label="Upload Mask", visible=True)
# Mode 2
image_selector = gr.Radio(examples, label="Select an example image", visible=False)
image_selected = gr.Image(None, label="Selected Image", visible=False, sources=[])
mask_selector = gr.Radio(masks, label="Select an example mask", visible=False)
mask_selected = gr.Image(None, label="Selected Mask", visible=False, sources=[])
output = gr.Image(type="numpy", label="Edge Image")
inputs = [
mode_selector,
upload_input,
upload_mask,
image_selector,
image_selected,
mask_selector,
mask_selected
]
mode_selector.change(
mode_selector_changed_inpainting,
inputs=[mode_selector, image_selector, mask_selector],
outputs=[upload_input, upload_mask, image_selector, image_selected, mask_selector, mask_selected]
)
image_selector.change(
mode_selector_changed,
inputs=[mode_selector, image_selector],
outputs=[upload_input, image_selector, image_selected]
)
mask_selector.change(
mode_selector_changed,
inputs=[mode_selector, mask_selector],
outputs=[upload_mask, mask_selector, mask_selected]
)
image_selected.change(
close_selected_image,
inputs=[image_selected],
outputs=[image_selector, image_selected]
)
mask_selected.change(
close_selected_mask,
inputs=[mask_selected],
outputs=[mask_selector, mask_selected]
)
gr.Interface(
fn=image_inpainting,
inputs=inputs,
outputs=output,
description="Upload an image and adjust the thresholds to perform edge detection",
flagging_mode='never'
)
if __name__ == "__main__":
demo.launch(allowed_paths=["/home/teng_aicv/Desktop/"]) |