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Upload 6 files
Browse files- Crime_Y5.pt +3 -0
- app.py +78 -0
- bourdon-defence-55.jpg +0 -0
- images (2).jpg +0 -0
- images (3).jpg +0 -0
- images (4).jpg +0 -0
Crime_Y5.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:d5fd2016db0538184c11e44c23681cf69e085c501c1367dc80b2c7401dc9ab39
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size 42250345
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app.py
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import gradio as gr
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from gradio.outputs import Label
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import cv2
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import requests
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import os
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import numpy as np
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from ultralytics import YOLO
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import yolov5
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file_urls = [
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'https://c8.alamy.com/zooms/9/382c1e254fe14207998df9ec56354291/wce5tj.jpg',
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'https://c8.alamy.com/comp/W20YYR/freight-container-on-the-back-of-a-truck-stuck-in-traffic-on-the-interstate-in-georgia-usa-W20YYR.jpg',
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'https://www.shutterstock.com/shutterstock/photos/318604739/display_1500/stock-photo-highway-and-container-truck-at-china-318604739.jpg'
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]
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def download_file(url, save_name):
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url = url
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if not os.path.exists(save_name):
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file = requests.get(url)
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open(save_name, 'wb').write(file.content)
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for i, url in enumerate(file_urls):
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download_file(
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file_urls[i],
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f"image_{i}.jpg"
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)
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# model_path = 'Container_YOLOV5'
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def yolov5_inference(
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image: gr.inputs.Image = None,
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model_path: gr.inputs.Dropdown = None,
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image_size: gr.inputs.Slider = 640,
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conf_threshold: gr.inputs.Slider = 0.25,
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iou_threshold: gr.inputs.Slider = 0.45 ):
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model = yolov5.load(model_path, device="cpu")
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model.conf = conf_threshold
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model.iou = iou_threshold
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results = model([image], size=image_size)
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crops = results.crop(save=False)
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img_crops = []
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for i in range(len(crops)):
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img_crops.append(crops[i]["im"][..., ::-1])
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return results.render()[0], img_crops
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inputs = [
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gr.inputs.Image(type="pil", label="Input Image"),
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gr.inputs.Dropdown(["Crime_Y5.pt","yolov5s.pt", "yolov5m.pt", "yolov5l.pt", "yolov5x.pt"], label="Model", default = 'Crime_Y5.pt'),
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gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
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]
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outputs = gr.outputs.Image(type="filepath", label="Output Image")
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outputs_crops = gr.Gallery(label="Object crop")
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title = "Container code detection - YOLO V5"
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description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
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examples = [['1.jpg', 'Crime_Y5.pt', 640, 0.35, 0.45]
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,['2.jpg', 'Crime_Y5.pt', 640, 0.35, 0.45]
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,['4.jpg', 'Crime_Y5.pt', 640, 0.35, 0.45]]
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demo_app = gr.Interface(
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fn=yolov5_inference,
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inputs=inputs,
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outputs=[outputs,outputs_crops],
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title=title,
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examples=examples,
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cache_examples=True,
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live=True,
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theme='huggingface',
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)
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demo_app.launch(debug=True, enable_queue=True, width=50, height=50)
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bourdon-defence-55.jpg
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images (2).jpg
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images (3).jpg
ADDED
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images (4).jpg
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