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import threading |
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import gradio as gr |
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import mtpnet_demo, yolop_demo |
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def Hex_to_RGB(hex_string): |
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r = int(hex_string[1:3], 16) |
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g = int(hex_string[3:5], 16) |
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b = int(hex_string[5:7], 16) |
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return [r, g, b] |
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class TaskThread(threading.Thread): |
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def __init__(self, func, args=()): |
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super(TaskThread, self).__init__() |
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self.func = func |
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self.args = args |
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def run(self): |
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self.result = self.func(*self.args) |
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def getResult(self): |
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try: |
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return self.result |
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except Exception: |
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return None |
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def detect(path, model, task, thickness, alpha_da, alpha_ll, color1, color2, color3): |
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global mtpnet, yolop |
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color = [Hex_to_RGB(color1), Hex_to_RGB(color2), Hex_to_RGB(color3)] |
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alpha = [alpha_da, alpha_ll] |
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result, result2, result3 = None, None, None |
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if 'mtpnet' in model: |
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mtpnet = TaskThread(mtpnet_demo.detect, args=(path, task, thickness, color, alpha)) |
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mtpnet.start() |
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if 'yolop' in model: |
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yolop = TaskThread(yolop_demo.detect, args=(path, task, thickness, color, alpha)) |
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yolop.start() |
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if 'mtpnet' in model: |
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mtpnet.join() |
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result = mtpnet.getResult() |
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if 'yolop' in model: |
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yolop.join() |
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result2 = yolop.getResult() |
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return result, result2 |
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gr.Interface( |
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fn=detect, |
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inputs= |
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[ |
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gr.Image(type='filepath', label="Input Image"), |
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gr.CheckboxGroup(["mtpnet", "yolop"], value=["mtpnet", "yolop"], label="Select model"), |
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gr.CheckboxGroup(["Vehicle detection", "Driving area segmentation", "Lane detection"], |
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value=["Vehicle detection", "Driving area segmentation", "Lane detection"], |
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label="Select task"), |
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gr.Slider(1, 5, value=2, label="Detection box line thickness", step=1), |
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gr.Slider(0.1, 1, value=0.5, label="Driving area transparency", step=0.1), |
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gr.Slider(0.1, 1, value=1, label="Lane Line area transparency", step=0.1), |
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gr.ColorPicker(label="Detection Box Color", value='#FFFF00'), |
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gr.ColorPicker(label="Driving Area Segmentation Color", value='#00FF00'), |
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gr.ColorPicker(label="Lane Line Color", value='#FF0000') |
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], |
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outputs=[ |
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gr.Image(label="Output image by mtpnet"), |
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gr.Image(label="Output image by yolop") |
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], |
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title="MtpNet 💪", |
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examples= |
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[ |
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["img/1.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/12.png", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/2.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/3.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/4.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/5.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/7.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/8.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/10.jpg", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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["img/11.png", ["mtpnet", "yolop"], ["Vehicle detection", "Driving area segmentation", "Lane detection"], 2, 0.5, 1, '#FFFF00', '#00FF00', '#FF0000'], |
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], |
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theme='default', |
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description="MtpNet 💪: demo for multi-task panoptic driving 🚗 perception network").launch(share=False) |
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