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from PIL import Image
import requests
import matplotlib.pyplot as plt
import torch
from torch import nn
from torchvision.models import resnet50
import torchvision.transforms as T
torch.set_grad_enabled(False);
import gradio as gr
import io
model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=True)
# Images
torch.hub.download_url_to_file('https://images.pexels.com/photos/461717/pexels-photo-461717.jpeg', 'horse.jpeg')
torch.hub.download_url_to_file('https://images.pexels.com/photos/5967799/pexels-photo-5967799.jpeg', 'turtle.jpeg')
# COCO classes
CLASSES = [
'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
'toothbrush'
]
# colors for visualization
COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
[0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]
# standard PyTorch mean-std input image normalization
transform = T.Compose([
T.Resize(800),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# for output bounding box post-processing
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=1)
def rescale_bboxes(out_bbox, size):
img_w, img_h = size
b = box_cxcywh_to_xyxy(out_bbox)
b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
return b
def fig2img(fig):
"""Convert a Matplotlib figure to a PIL Image and return it"""
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
return Image.open(buf)
def plot_results(pil_img, prob, boxes):
plt.figure(figsize=(16,10))
plt.imshow(pil_img)
ax = plt.gca()
colors = COLORS * 100
for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
fill=False, color=c, linewidth=3))
cl = p.argmax()
text = f'{CLASSES[cl]}: {p[cl]:0.2f}'
ax.text(xmin, ymin, text, fontsize=15,
bbox=dict(facecolor='yellow', alpha=0.5))
plt.axis('off')
return fig2img(plt)
def detr(im):
# mean-std normalize the input image (batch-size: 1)
img = transform(im).unsqueeze(0)
# propagate through the model
outputs = model(img)
# keep only predictions with 0.7+ confidence
probas = outputs['pred_logits'].softmax(-1)[0, :, :-1]
keep = probas.max(-1).values > 0.9
# convert boxes from [0; 1] to image scales
bboxes_scaled = rescale_bboxes(outputs['pred_boxes'][0, keep], im.size)
return plot_results(im, probas[keep], bboxes_scaled)
inputs = gr.inputs.Image(type='pil', label="Original Image", shape=(600,600))
outputs = gr.outputs.Image(type="pil",label="Output Image")
examples = [
['horse.jpeg'],
['turtle.jpeg']
]
title = "AIBC DETR demo"
description = "aibc object detection demo"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2005.12872'>End-to-End Object Detection with Transformers</a> | <a href='https://github.com/facebookresearch/detr'>Github Repo</a></p>"
gr.Interface(detr, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()
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