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README.md

@@ -1,13 +1,54 @@
 ---
-title: Yolov5 Farm Cattle
-emoji: 🐠
-colorFrom: red
+title: YOLOv5 Cattle Counter
+emoji: 
+colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 4.31.4
+sdk_version: 4.12.0
 app_file: app.py
 pinned: false
 license: ecl-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# YOLOv5 Cattle Counter
+
+This project uses a YOLOv5 model to detect and count cattle in images. The model was trained using custom data and is deployedusing Gradio for an interactive web interface.
+
+
+## Developer
+
+Developed by Ramon Mayor Martins (2023)
+
+- Email: [[email protected]](mailto:[email protected])
+- Homepage: [https://rmayormartins.github.io/](https://rmayormartins.github.io/)
+- Twitter: [@rmayormartins](https://twitter.com/rmayormartins)
+- GitHub: [https://github.com/rmayormartins](https://github.com/rmayormartins)
+- my Radio Callsign (PU4MAY) Brazil
+
+## Model Information
+- **Model:** YOLOv5
+- **Task:** Object Detection
+- **Classes:** Cattle
+
+## How to Use
+1. Upload an image of cattle.
+2. The model will detect and count the number of cattle in the image.
+3. The output will display the image with bounding boxes around detected cattle and the total count.
+
+## Example
+You can use the provided example image to test the model.
+
+## Installation
+To install the necessary dependencies, run:
+```bash
+pip install -r requirements.txt
+
+## License
+
+This project is released under the ECL-2.0 license.
+
+
+---
+*Check out the configuration reference at [Hugging Face Spaces Config Reference](https://huggingface.co/docs/hub/spaces-config-reference).*
+
+

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import torch
+import matplotlib.pyplot as plt
+
+# modelo
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='bestyolo5.pt')
+
+def detect(img):
+    img_arr = np.array(img)
+    results = model(img_arr)
+
+    fig, ax = plt.subplots()
+    ax.imshow(img_arr)
+
+    cattle_count = 0
+    for *xyxy, conf, cls in results.xyxy[0].cpu().numpy():
+        x1, y1, x2, y2 = map(int, xyxy)
+        label = model.names[int(cls)]
+        if label == 'cattle':
+            cattle_count += 1
+        ax.add_patch(plt.Rectangle((x1, y1), x2-x1, y2-y1, fill=False, color='red', linewidth=2))
+        ax.text(x1, y1, f'{label} {conf:.2f}', color='white', fontsize=8, bbox={'facecolor': 'red', 'alpha': 0.5})
+
+    plt.axis('off')
+
+    fig.canvas.draw()
+    pil_img = Image.fromarray(np.array(fig.canvas.renderer._renderer))
+    plt.close(fig)
+
+    return pil_img, cattle_count
+
+# gradio
+iface = gr.Interface(
+    fn=detect,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), gr.Textbox(label="Number of Cattle Detected")],
+    title="YOLOv5 Cattle Counter",
+    description="Object detector trained to count cattle using YOLOv5.",
+    examples=[["example1.jpg"]]
+)
+
+if __name__ == "__main__":
+    iface.launch()

bestyolo5.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69283e3a5d8edeb906cdf04cbda320a7a542f191fd28302d4057a54d4ad57f70
+size 14390952

requirements.txt

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+gradio==4.29.0
+numpy
+Pillow
+torch
+matplotlib
+
+
+