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# v0.0.2 by babaochou
import cv2
import numpy as np
import gradio as gr
class ImageProcesser:
def __init__(self):
pass
# Function -- Turn Image into Sketch with modes select-able
def sketch_image(self, image, pencilShadow, outlineSimplify, R, G, B, mode):
try:
outlineBlur = (2*outlineSimplify)+1
lineColour = (R, G, B)
# if (bgMode == 'Remove Background'):
# # image = removeBackground(image)
# None
# elif (bgMode == 'Keep Background'):
# None
# Turn image into grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Make a inverted grayscale for after-processing
gray_image_inverted = cv2.bitwise_not(gray_image)
# Apply GaussianBlur to the inverted image
blurred_gray_image = cv2.GaussianBlur(
gray_image_inverted, (pencilShadow, pencilShadow), 0)
# Make a inverted GaussianBlur for after-processing
inverted_blurred_image = cv2.bitwise_not(blurred_gray_image)
# For Canny-Algorithm
edgeFindingImage = cv2.GaussianBlur(
gray_image, (outlineBlur, outlineBlur), 0)
# Using Canny-Algorithm to get better outline from the grayscale
edges = cv2.bitwise_not(cv2.Canny(edgeFindingImage, 100, 200))
cv2.normalize(
edges, edges, 0, 255, norm_type=cv2.NORM_MINMAX)
# Craete a mask for outline colouring
edge_mask = np.zeros_like(image)
edge_mask[:, :] = lineColour
# Changing outline colour
edges = cv2.bitwise_or(
edge_mask, cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR))
# # Split the 3-channels
# _b, _g, _r = cv2.split(edges)
# # Create a alpha channel
# _a = np.full(image.shape, A)
# # Merge into a RGBA
# edges = cv2.merge((_b, _g, _r, _a))
# # Changing back to 3-channels
# edges = cv2.cvtColor(edges, cv2.COLOR_BGRA2BGR)
# If user want outline only
if (mode == 'Outline Only'):
return edges
elif (mode == 'Grayscale'):
return gray_image
# If user want shadow from the grayscale as well
elif (mode == 'With Shadow'):
pencil_sketch_image = cv2.divide(
gray_image, inverted_blurred_image, scale=226.0)
# Chaning the image into 4-channels
pencil_sketch_image = cv2.cvtColor(
pencil_sketch_image, cv2.COLOR_GRAY2BGR)
# Giving a better outline
combined_image = cv2.bitwise_and(pencil_sketch_image, edges)
return combined_image
elif (mode == 'Negative'):
pencil_sketch_image = cv2.divide(
gray_image, inverted_blurred_image, scale=226.0)
# Giving a better outline then make a negative
combined_image = cv2.bitwise_not(pencil_sketch_image, edges)
return combined_image
except Exception as error:
return None
class GradioInterface:
def __init__(self):
self.imageProcesser = ImageProcesser()
with gr.Blocks() as input_blocks:
with gr.Row():
imageInput = gr.Image(label="Original")
imageOutput = gr.Image(label='Processed')
btnGenerate = gr.Button(value='Generate')
with gr.Row():
sliderShadow = gr.Slider(minimum=1, maximum=27, value=9, step=2,
label="Shadow Strengthen", info="9 is for outline only, 21 is with shadows")
sliderOutline = gr.Slider(minimum=0, maximum=7, value=0, step=1,
label="Outline Simplify", info="")
with gr.Row():
sldierR = gr.Slider(label='R', minimum=0,
maximum=255, step=1, value=0)
sliderG = gr.Slider(label='G', minimum=0,
maximum=255, step=1, value=0)
sliderB = gr.Slider(label='B', minimum=0,
maximum=255, step=1, value=0)
# sliderA = gr.Slider(label='A', minimum=0,
# maximum=255, step=1, value=0)
radioMode = gr.Radio(["With Shadow", "Outline Only", "Negative", 'Grayscale'],
label="Mode", value='With Shadow')
# radioBackground = gr.Radio(['Keep Background', 'Remove Background'],
# value='Keep Background')
# button to start processing
btnGenerate.click(fn=self.imageProcesser.sketch_image,
inputs=[imageInput, sliderShadow, sliderOutline, sldierR, sliderG, sliderB, radioMode], outputs=imageOutput)
input_blocks.launch()
if __name__ == "__main__":
gradio_interface = GradioInterface() |