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Created app.py
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app.py
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from transformers import pipeline
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from transformers import DetrFeatureExtractor, DetrForObjectDetection
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from PIL import Image, ImageDraw, ImageFont
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import gradio as gr
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# Initialize another model and feature extractor
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feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
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model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
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# Initialize the object detection pipeline
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object_detector = pipeline("object-detection", model = model, feature_extractor = feature_extractor)
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# Draw bounding box definition
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def draw_bounding_box(im, score, label, xmin, ymin, xmax, ymax, index, num_boxes):
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""" Draw a bounding box. """
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# Draw the actual bounding box
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outline = ' '
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if label in ['truck', 'car', 'motorcycle', 'bus']:
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outline = 'red'
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elif label in ['person', 'bicycle']:
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outline = 'green'
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else:
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outline = 'blue'
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im_with_rectangle = ImageDraw.Draw(im)
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im_with_rectangle.rounded_rectangle((xmin, ymin, xmax, ymax), outline = outline, width = 3, radius = 10)
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# Return the result
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return im
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def detect_image(im):
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# Perform object detection
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bounding_boxes = object_detector(im)
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# Iteration elements
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num_boxes = len(bounding_boxes)
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index = 0
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# Draw bounding box for each result
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for bounding_box in bounding_boxes:
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if bounding_box['label'] in ['person','motorcycle','bicycle', 'truck', 'car','bus']:
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box = bounding_box['box']
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#Draw the bounding box
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output_image = draw_bounding_box(im, bounding_box['score'],
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bounding_box['label'],
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box['xmin'], box['ymin'],
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box['xmax'], box['ymax'],
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index, num_boxes)
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index += 1
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return output_image
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iface = gr.Interface(detect_image, gr.inputs.Image(type = 'pil'), gr.outputs.Image()).launch(debug = True)
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