Duplicate from roxas010394/parts-of-cars

.gitattributes

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README.md

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+---
+title: Parts Of Cars
+emoji: 👁
+colorFrom: indigo
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.20.1
+app_file: app.py
+pinned: false
+license: unknown
+duplicated_from: roxas010394/parts-of-cars
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c6225903bf0151ed639859338fe490946f1c5ae9e2a515ed5a9df709c4acf87
+size 23798722

requirements.txt

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+ultralytics
+opencv-python
+numpy
+matplotlib
+seaborn