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Crime_Y5.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d5fd2016db0538184c11e44c23681cf69e085c501c1367dc80b2c7401dc9ab39
+size 42250345

app.py

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