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from transformers import DetrImageProcessor, DetrForObjectDetection
from transformers import YolosImageProcessor, YolosForObjectDetection
import torch
from PIL import Image
import io
# Load transformer-based model (Yolos or DETR)
def load_model(model_uri: str):
"""Load Transformer model"""
""" - Doc DETR: https://huggingface.co/docs/transformers/en/model_doc/detr"""
""" - Doc Yolos: https://huggingface.co/docs/transformers/en/model_doc/yolos"""
if "detr" in model_uri:
# you can specify the revision tag if you don't want the timm dependency
processor = DetrImageProcessor.from_pretrained(model_uri, revision="no_timm")
model = DetrForObjectDetection.from_pretrained(model_uri, revision="no_timm")
elif "yolos" in model_uri:
processor = YolosImageProcessor.from_pretrained(model_uri)
model = YolosForObjectDetection.from_pretrained(model_uri)
else:
processor = None
model = None
return processor, model
def object_detection(processor, model, image_bytes):
"""Perform object detection task"""
print('Object detection prediction...')
#url = "http://images.cocodataset.org/val2017/000000039769.jpg"
#image = Image.open(requests.get(url, stream=True).raw)
img = Image.open(io.BytesIO(image_bytes))
inputs = processor(images=img, return_tensors="pt")
# print('inputs', inputs)
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([img.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
return results
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