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| # from PIL import Image | |
| from transformers import DetrFeatureExtractor | |
| from transformers import DetrForObjectDetection | |
| import torch | |
| # import numpy as np | |
| def object_count(picture): | |
| feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") | |
| encoding = feature_extractor(picture, return_tensors="pt") | |
| model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") | |
| outputs = model(**encoding) | |
| # keep only predictions of queries with 0.9+ confidence (excluding no-object class) | |
| probas = outputs.logits.softmax(-1)[0, :, :-1] | |
| keep = probas.max(-1).values > 0.7 | |
| count = 0 | |
| for i in keep: | |
| if i: | |
| count=count+1 | |
| return "About " + str(count) +" common objects were detected" | |
| # object_count("toothbrush.jpg") | |
| import gradio as gr | |
| interface = gr.Interface(object_count, gr.inputs.Image(shape=(640, 480)), "text").launch() | |