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a49e475 9f83fdf a49e475 ea524bf a49e475 ea524bf a49e475 ea524bf a49e475 ea524bf a49e475 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import gradio as gr
import numpy as np
import cv2 # type: ignore
def transform_cv2(frame, transform):
if transform == "cartoon":
# prepare color
img_color = cv2.pyrDown(cv2.pyrDown(frame))
for _ in range(6):
img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
img_color = cv2.pyrUp(cv2.pyrUp(img_color))
# prepare edges
img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
img_edges = cv2.adaptiveThreshold(
cv2.medianBlur(img_edges, 7),
255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
9,
2,
)
img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
# combine color and edges
img = cv2.bitwise_and(img_color, img_edges)
return img
elif transform == "edges":
# perform edge detection
img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
return img
else:
return np.flipud(frame)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
transform = gr.Dropdown(
choices=["cartoon", "edges", "flip"],
value="flip",
label="Transformation",
)
input_img = gr.Image(sources=["webcam"], type="numpy")
with gr.Column():
output_img = gr.Image(streaming=True)
dep = input_img.stream(
transform_cv2,
[input_img, transform],
[output_img],
time_limit=30,
stream_every=0.1,
concurrency_limit=30,
)
if __name__ == "__main__":
demo.launch()
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