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from transformers import AutoFeatureExtractor, AutoModelForObjectDetection
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from random import choice
from PIL import Image
import os
from matplotlib import rcParams, font_manager
import streamlit as st
import urllib.request
import requests
extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")
from transformers import pipeline
pipe = pipeline('object-detection', model=model, feature_extractor=extractor)
my_bar = st.progress(0)
img_url = st.text_input('Image URL', 'https://images.unsplash.com/photo-1556911220-bff31c812dba?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=2468&q=80')
my_bar.progress(20)
img_data = requests.get(img_url).content
with open('detect.jpg', 'wb') as handler:
handler.write(img_data)
my_bar.progress(60)
output = pipe(img_url)
my_bar.progress(80)
fpath = "Poppins-SemiBold.ttf"
prop = font_manager.FontProperties(fname=fpath)
img = Image.open('detect.jpg')
plt.figure(dpi=2400)
# Create figure and axes
fig, ax = plt.subplots()
# Display the image
ax.imshow(img)
colors = ["#ef4444", "#f97316", "#eab308", "#84cc16", "#06b6d4", "#6366f1"]
# Create a Rectangle patch
for prediction in output:
selected_color = choice(colors)
x, y, w, h = prediction['box']['xmin'], prediction['box']['ymin'], prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
rect = patches.FancyBboxPatch((x, y), w, h, linewidth=1.25, edgecolor=selected_color, facecolor='none', boxstyle="round,pad=-0.0040,rounding_size=10",)
ax.add_patch(rect)
plt.text(x, y-25, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontsize=5, color=selected_color, fontproperties=prop)
plt.axis('off')
plt.savefig('detect-bbox.jpg', dpi=1200, bbox_inches='tight')
image = Image.open('detect-bbox.jpg')
st.image(image, caption='DETR Image')
plt.show()
my_bar.progress(100) |