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from collections import Counter
import cv2
import gradio as gr
import numpy as np
from PIL import Image
from ultralytics import YOLO
# ==========================================================
# Load Model
# ==========================================================
model = YOLO("best.pt")
# ==========================================================
# Example Images
# ==========================================================
example_images = []
if os.path.exists("examples"):
for file in sorted(os.listdir("examples")):
if file.lower().endswith((".jpg", ".jpeg", ".png")):
example_images.append([os.path.join("examples", file)])
# ==========================================================
# Detection Function
# ==========================================================
def detect(image, conf, iou):
results = model.predict(
source=image,
conf=conf,
iou=iou,
verbose=False
)
result = results[0]
plotted = result.plot()
plotted = cv2.cvtColor(plotted, cv2.COLOR_BGR2RGB)
detected = []
for cls in result.boxes.cls.tolist():
detected.append(model.names[int(cls)])
counter = Counter(detected)
table = []
for name, count in sorted(counter.items()):
table.append([name, count])
return Image.fromarray(plotted), table
# ==========================================================
# Metric Images
# ==========================================================
metric_files = [
"metrics/results.png",
"metrics/P_curve.png",
"metrics/R_curve.png",
"metrics/PR_curve.png",
"metrics/F1_curve.png",
"metrics/confusion_matrix.png"
]
metric_components = []
for path in metric_files:
if os.path.exists(path):
metric_components.append(gr.Image(value=path, label=os.path.basename(path)))
# ==========================================================
# About Text
# ==========================================================
about = """
# Warehouse Vision AI
### Industrial Object Detection using YOLOv8
This project detects warehouse objects using a custom-trained YOLO model.
### Features
- Industrial Rack Detection
- KLT Box Detection
- Computer Hardware Detection
- Safety Equipment Detection
- Warehouse Object Localization
### Framework
- Ultralytics YOLO
- Gradio
- Hugging Face Spaces
### Author
Omkar Kalburgi
"""
# ==========================================================
# UI
# ==========================================================
with gr.Blocks(title="Warehouse Vision AI") as demo:
gr.Markdown(
"""
# 📦 Warehouse Vision AI
### YOLO-based Industrial Warehouse Object Detection
Upload an image or try one of the sample images.
"""
)
with gr.Tabs():
# --------------------------------------------------
with gr.Tab("🔍 Detection"):
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Input Image")
conf = gr.Slider(
0.1,
1.0,
value=0.25,
step=0.05,
label="Confidence Threshold",
)
iou = gr.Slider(
0.1,
1.0,
value=0.45,
step=0.05,
label="IoU Threshold",
)
btn = gr.Button("Run Detection")
with gr.Column():
output = gr.Image(label="Prediction")
table = gr.Dataframe(
headers=["Class", "Count"],
datatype=["str", "number"],
interactive=False,
label="Detected Objects",
)
btn.click(
detect,
inputs=[image, conf, iou],
outputs=[output, table],
)
# --------------------------------------------------
with gr.Tab("🧪 Sample Images"):
gr.Markdown("Click any image below to test the model.")
sample_input = gr.Image(type="pil")
sample_output = gr.Image()
sample_table = gr.Dataframe(
headers=["Class", "Count"],
interactive=False,
)
gr.Examples(
examples=example_images,
inputs=sample_input,
)
sample_btn = gr.Button("Run Detection")
sample_btn.click(
detect,
inputs=[sample_input, conf, iou],
outputs=[sample_output, sample_table],
)
# --------------------------------------------------
with gr.Tab("📈 Model Performance"):
gr.Markdown("Training Metrics")
for img in metric_components:
img.render()
# --------------------------------------------------
with gr.Tab("📖 About"):
gr.Markdown(about)
demo.launch() |