Spaces:
Sleeping
Sleeping
import io | |
import requests | |
import numpy as np | |
import gradio as gr | |
from PIL import Image | |
import matplotlib.pyplot as plt | |
from transformers import pipeline | |
# Load the pipeline | |
obj_detector = pipeline( | |
task="object-detection", | |
model="facebook/detr-resnet-50" | |
) | |
# Object detection utilities | |
def load_image_from_url(url: str): | |
return Image.open(requests.get(url, stream=True).raw).convert("RGB") | |
def render_results_in_image(img, detection_results): | |
plt.figure(figsize=(16, 10)) | |
plt.imshow(img) | |
ax = plt.gca() | |
for prediction in detection_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)}%" | |
) | |
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 | |
def summarize_detection_results(detection_results): | |
summary = {} | |
for prediction in detection_results: | |
label = prediction["label"] | |
if label in summary: | |
summary[label] += 1 | |
else: | |
summary[label] = 1 | |
summary_string = "In this image, there are " | |
for i, (label, count) in enumerate(summary.items()): | |
summary_string += f"{str(count)} {label}" | |
if count > 1: | |
summary_string += "s" | |
summary_string += ", " | |
if i == len(summary) - 2: | |
summary_string += "and " | |
# remove the trailing comma and space | |
summary_string = summary_string.rstrip(", ") + "." | |
return summary_string | |
def detect_objects(image): | |
detection_results = obj_detector(image) | |
processed_image = render_results_in_image(image, detection_results) | |
summary_string = summarize_detection_results(detection_results) | |
return processed_image, summary_string | |
obj_detection_interface = gr.Interface( | |
fn=detect_objects, | |
inputs=gr.Image(label="Input Image", type="pil"), | |
outputs=[ | |
gr.Image(label="Output image with predicted objects", type="pil"), | |
gr.Textbox(label="Object detection summary") | |
], | |
title="Object Detection Application", | |
description="This app detects objects from an image.", | |
examples=["./examples/image1.jpg"] | |
) |