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import streamlit.components.v1 as components
import numpy
import sahi.predict
import sahi.utils
from PIL import Image
import base64
import io
def sahi_mmdet_inference(
image,
detection_model,
slice_height=512,
slice_width=512,
overlap_height_ratio=0.2,
overlap_width_ratio=0.2,
image_size=640,
postprocess_type="UNIONMERGE",
postprocess_match_metric="IOS",
postprocess_match_threshold=0.5,
postprocess_class_agnostic=False,
):
# standard inference
prediction_result_1 = sahi.predict.get_prediction(
image=image, detection_model=detection_model, image_size=image_size
)
visual_result_1 = sahi.utils.cv.visualize_object_predictions(
image=numpy.array(image),
object_prediction_list=prediction_result_1.object_prediction_list,
)
output_1 = Image.fromarray(visual_result_1["image"])
# sliced inference
prediction_result_2 = sahi.predict.get_sliced_prediction(
image=image,
detection_model=detection_model,
image_size=image_size,
slice_height=slice_height,
slice_width=slice_width,
overlap_height_ratio=overlap_height_ratio,
overlap_width_ratio=overlap_width_ratio,
postprocess_type=postprocess_type,
postprocess_match_metric=postprocess_match_metric,
postprocess_match_threshold=postprocess_match_threshold,
postprocess_class_agnostic=postprocess_class_agnostic,
)
visual_result_2 = sahi.utils.cv.visualize_object_predictions(
image=numpy.array(image),
object_prediction_list=prediction_result_2.object_prediction_list,
)
output_2 = Image.fromarray(visual_result_2["image"])
return output_1, output_2
def pillow_to_base64(image: Image.Image):
in_mem_file = io.BytesIO()
image.save(in_mem_file, format="PNG")
img_bytes = in_mem_file.getvalue() # bytes
image_str = base64.b64encode(img_bytes).decode("utf-8")
base64_src = f"data:image/png;base64,{image_str}"
return base64_src
def imagecompare(
img1: str,
img2: str,
label1: str = "1",
label2: str = "2",
width: int = 700,
show_labels: bool = True,
starting_position: int = 50,
make_responsive: bool = True,
):
"""Create a new juxtapose component.
Parameters
----------
img1: str, PosixPath, PIL.Image or URL
Input image to compare
img2: str, PosixPath, PIL.Image or URL
Input image to compare
label1: str or None
Label for image 1
label2: str or None
Label for image 2
width: int or None
Width of the component in px
show_labels: bool or None
Show given labels on images
starting_position: int or None
Starting position of the slider as percent (0-100)
make_responsive: bool or None
Enable responsive mode
Returns
-------
static_component: Boolean
Returns a static component with a timeline
"""
# prepare images
img_width, img_height = img1.size
h_to_w = img_height / img_width
height = (width * h_to_w) * 0.95
img1 = pillow_to_base64(sahi.utils.cv.read_image_as_pil(img1))
img2 = pillow_to_base64(sahi.utils.cv.read_image_as_pil(img2))
# load css + js
cdn_path = "https://cdn.knightlab.com/libs/juxtapose/latest"
css_block = f'<link rel="stylesheet" href="{cdn_path}/css/juxtapose.css">'
js_block = f'<script src="{cdn_path}/js/juxtapose.min.js"></script>'
# write html block
htmlcode = f"""
{css_block}
{js_block}
<div id="foo"style="height: {height}; width: {width or '%100'};"></div>
<script>
slider = new juxtapose.JXSlider('#foo',
[
{{
src: '{img1}',
label: '{label1}',
}},
{{
src: '{img2}',
label: '{label2}',
}}
],
{{
animate: true,
showLabels: {'true' if show_labels else 'false'},
showCredits: true,
startingPosition: "{starting_position}%",
makeResponsive: {'true' if make_responsive else 'false'},
}});
</script>
"""
static_component = components.html(htmlcode, height=height, width=width)
return static_component
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