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import torch |
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import cv2 |
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import gradio as gr |
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import numpy as np |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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from transformers import OwlViTProcessor, OwlViTForObjectDetection |
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import os |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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else: |
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device = torch.device("cpu") |
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model = OwlViTForObjectDetection.from_pretrained("google/owlvit-large-patch14").to(device) |
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model.eval() |
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processor = OwlViTProcessor.from_pretrained("google/owlvit-large-patch14") |
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def query_image(img, text_queries, score_threshold): |
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text_queries = text_queries.split(",") |
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img = np.array(img) |
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target_sizes = torch.Tensor([img.shape[:2]]) |
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inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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outputs.logits = outputs.logits.cpu() |
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outputs.pred_boxes = outputs.pred_boxes.cpu() |
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results = processor.post_process(outputs=outputs, target_sizes=target_sizes) |
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boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"] |
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font = cv2.FONT_HERSHEY_SIMPLEX |
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for box, score, label in zip(boxes, scores, labels): |
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box = [int(i) for i in box.tolist()] |
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if score >= score_threshold: |
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img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5) |
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if box[3] + 25 > 768: |
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y = box[3] - 10 |
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else: |
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y = box[3] + 25 |
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img = cv2.putText( |
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img, text_queries[label], (box[0], y), font, 1, (255,0,0), 2, cv2.LINE_AA |
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) |
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return img |
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description = """ |
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\n\nYou can use OWL-ViT to query images with text descriptions of any object. |
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To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You |
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can also use the score threshold slider to set a threshold to filter out low probability predictions. |
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""" |
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upload = gr.Interface( |
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query_image, |
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inputs=[gr.Image(source="upload"), |
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"text", |
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gr.Slider(0, 1, value=0.1)], |
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outputs="image", |
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title="Zero-Shot Object Detection with OWL-ViT", |
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description=description, |
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examples=[os.path.join("examples","IMGP0178.jpg")]) |
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web = gr.Interface( |
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query_image, |
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inputs=[gr.Image(source="webcam"), |
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"text", |
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gr.Slider(0, 1, value=0.1)], |
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outputs="image", |
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title="Zero-Shot Object Detection with OWL-ViT", |
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description=description, |
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examples="./examples/IMGP0178.jpg") |
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demo = gr.TabbedInterface(interface_list=[upload, web], |
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tab_names=["From a File", "From your Webcam"]) |
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demo.launch() |
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with gr.Blocks() as demo: |
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with gr.Column(): |
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with gr.Tab("Upload image"): |
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with gr.Row(): |
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with gr.Column(): |
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inputs_file=[gr.Image(source="webcam"), |
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"text", |
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gr.Slider(0, 1, value=0.1)] |
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submit_btn = gr.Button("Submit") |
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im_output = gr.Image() |
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with gr.Tab("Capture image with webcam"): |
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with gr.Row(): |
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with gr.Column(): |
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inputs_web=[gr.Image(source="webcam"), |
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"text", |
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gr.Slider(0, 1, value=0.1)] |
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submit_btn_web = gr.Button("Submit") |
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web_output = gr.Image() |
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gr.Examples(["examples/IMGP0178.jpg"]) |
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submit_btn.click(fn=query_image, inputs= inputs_file, outputs = im_output) |
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submit_btn_web.click(fn=query_image, inputs= inputs_web, outputs = web_output) |
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demo.launch() |
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