Upload stat_lab_10.py

stat_lab_10.py

@@ -0,0 +1,63 @@
+# -*- 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}"
+    )
+
+
+
+
+