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import gradio as gr
import numpy as np
import cv2
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)
css=""".my-group {max-width: 500px !important; max-height: 500px !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
value="flip", label="Transformation")
input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
input_img.stream(transform_cv2, [input_img, transform], [input_img], time_limit=30, stream_every=0.1)
demo.launch()
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