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from ultralytics import YOLO |
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import matplotlib.pyplot as plt |
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import gradio as gr |
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import cv2 |
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import numpy as np |
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import random |
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import seaborn as sns |
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from PIL import Image |
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import io |
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def predict(path:str): |
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model = YOLO("yolov8n.yaml") |
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model = YOLO("best.pt") |
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image = cv2.imread(path) |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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results = model.predict(source=path) |
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paleta= sns.color_palette("bright", 17) |
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fig = plt.figure() |
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vectObjs = results[0].masks.xy |
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resNumClase = results[0].boxes.cls.numpy().astype(int) |
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conf = results[0].boxes.conf.numpy() |
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for i in range(len(vectObjs)): |
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objDet = vectObjs[i].astype(int) |
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color = (paleta[i][0]*255, paleta[i][1]*255, paleta[i][2]*255) |
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image = cv2.polylines(image, [objDet], True, color, 4) |
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plt.text(objDet[0][0], objDet[0][1], results[0].names[resNumClase[i]]+" "+ str(conf[i]), bbox=dict(facecolor=paleta[i], alpha=0.5)) |
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plt.imshow(image) |
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plt.axis('off') |
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return plt |
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gr.Interface(fn=predict, |
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inputs=gr.components.Image(type="filepath", label="Input"), |
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outputs=gr.Plot(label="Resultado de detección de objetos con regularizacion")).launch() |
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