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from transformers import pipeline
import io
import matplotlib.pyplot as plt
import torch
from PIL import Image
def render_results_in_image(in_pil_img, in_results):
plt.figure(figsize=(16, 10))
plt.imshow(in_pil_img)
ax = plt.gca()
for prediction in in_results:
x, y = prediction['box']['xmin'], prediction['box']['ymin']
w = prediction['box']['xmax'] - prediction['box']['xmin']
h = prediction['box']['ymax'] - prediction['box']['ymin']
ax.add_patch(plt.Rectangle((x, y),
w,
h,
fill=False,
color="green",
linewidth=2))
ax.text(
x,
y,
f"{prediction['label']}: {round(prediction['score']*100, 1)}%",
color='red'
)
plt.axis("off")
# Save the modified image to a BytesIO object
img_buf = io.BytesIO()
plt.savefig(img_buf, format='png',
bbox_inches='tight',
pad_inches=0)
img_buf.seek(0)
modified_image = Image.open(img_buf)
# Close the plot to prevent it from being displayed
plt.close()
return modified_image
od_pipe = pipeline("object-detection", "facebook/detr-resnet-50")
import gradio as gr
def get_pipeline_prediction(pil_image):
#first get the pipeline output given the pil image
pipeline_output = od_pipe(pil_image)
#then process the image using the pipeline output
processed_image = render_results_in_image(pil_image, pipeline_output)
return processed_image
demo = gr.Interface(
fn= get_pipeline_prediction,
inputs=gr.Image(label="Input Image",
type="pil"),
outputs=gr.Image(label="Output Image with predictions",
type="pil"),
title="Object Detection API",
description="Just upload your image and let ObjectDetect API work its magic, revealing the objects waiting to be discovered"
)
demo.launch() |