stat_lab_10.py

# -*- coding: utf-8 -*-
"""stat_lab_10.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1M9jt20Xv08CFH0RJOpWe8aXT62PqGrKu
"""

!python -m pip install transformers accelerate sentencepiece emoji pythainlp --quiet
!python -m pip install --no-deps thai2transformers==0.1.2 --quiet

"""# image Detection"""

!pip install timm

"""## pipline"""

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("object-detection", model="facebook/detr-resnet-50")

"""## Load model"""

# Load model directly
from transformers import AutoFeatureExtractor, AutoModelForObjectDetection

extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")

"""## Use model"""

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

inputs = processor(images=image, return_tensors="pt")
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([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
            f"Detected {model.config.id2label[label.item()]} with confidence "
            f"{round(score.item(), 3)} at location {box}"
    )