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import time
import gradio as gr
from PIL import Image
import pandas as pd
from advanced_ocr import AdvancedLicensePlateOCR, get_available_models
import os
from typing import List, Dict, Tuple
class OCRModelComparison:
def __init__(self):
self.ocr_system = AdvancedLicensePlateOCR()
self.results_cache = {}
def benchmark_single_image(self, image: Image.Image, model_keys: List[str]) -> Dict:
results = {
"image_size": image.size,
"models_tested": len(model_keys),
"results": []
}
for model_key in model_keys:
try:
start_time = time.time()
extraction_result = self.ocr_system.extract_text_with_model(
image, model_key, use_preprocessing=True
)
end_time = time.time()
processing_time = end_time - start_time
model_info = self.ocr_system.models[model_key]
result_entry = {
"model_key": model_key,
"model_name": model_info["name"],
"model_type": model_info["type"],
"processing_time": round(processing_time, 3),
"success": "error" not in extraction_result,
"best_result": extraction_result.get("best_result", "Error"),
"confidence": extraction_result.get("confidence", 0.0),
"extractions_count": len(extraction_result.get("extractions", [])),
"status": "✅ Success" if "error" not in extraction_result else f"❌ {extraction_result.get('error', 'Unknown error')}"
}
results["results"].append(result_entry)
except Exception as e:
result_entry = {
"model_key": model_key,
"model_name": self.ocr_system.models.get(model_key, {}).get("name", "Unknown"),
"model_type": self.ocr_system.models.get(model_key, {}).get("type", "Unknown"),
"processing_time": 0.0,
"success": False,
"best_result": f"Exception: {str(e)}",
"confidence": 0.0,
"extractions_count": 0,
"status": f"❌ Exception: {str(e)}"
}
results["results"].append(result_entry)
return results
def create_comparison_table(self, benchmark_results: Dict) -> pd.DataFrame:
if not benchmark_results.get("results"):
return pd.DataFrame()
df_data = []
for result in benchmark_results["results"]:
df_data.append({
"Model": result["model_name"],
"Type": result["model_type"],
"Status": result["status"],
"Extracted Text": result["best_result"],
"Confidence": f"{result['confidence']:.2f}",
"Processing Time (s)": result["processing_time"],
"Variants Processed": result["extractions_count"]
})
return pd.DataFrame(df_data)
def get_best_model_recommendation(self, benchmark_results: Dict) -> str:
if not benchmark_results.get("results"):
return "No results available"
successful_results = [r for r in benchmark_results["results"] if r["success"]]
if not successful_results:
return "❌ No models succeeded in text extraction"
best_by_confidence = max(successful_results, key=lambda x: x["confidence"])
fastest = min(successful_results, key=lambda x: x["processing_time"])
recommendation = f"""
🏆 **Best Results:**
**Highest Confidence:** {best_by_confidence['model_name']}
- Text: "{best_by_confidence['best_result']}"
- Confidence: {best_by_confidence['confidence']:.2f}
- Time: {best_by_confidence['processing_time']:.3f}s
**Fastest Processing:** {fastest['model_name']}
- Text: "{fastest['best_result']}"
- Time: {fastest['processing_time']:.3f}s
- Confidence: {fastest['confidence']:.2f}
**Recommendation:**
{"Use " + best_by_confidence['model_name'] + " for best accuracy" if best_by_confidence != fastest else "Best overall: " + best_by_confidence['model_name']}
"""
return recommendation
def compare_ocr_models(image, selected_models):
if image is None:
return "Please upload an image", pd.DataFrame(), "No comparison performed"
if not selected_models:
return "Please select at least one model", pd.DataFrame(), "No models selected"
try:
comparator = OCRModelComparison()
if isinstance(image, str):
image = Image.open(image)
benchmark_results = comparator.benchmark_single_image(image, selected_models)
comparison_table = comparator.create_comparison_table(benchmark_results)
recommendation = comparator.get_best_model_recommendation(benchmark_results)
status_msg = f"✅ Comparison completed! Tested {len(selected_models)} models on image size {benchmark_results['image_size']}"
return status_msg, comparison_table, recommendation
except Exception as e:
error_msg = f"❌ Error during comparison: {str(e)}"
return error_msg, pd.DataFrame(), "Comparison failed"
def create_model_comparison_app():
models = get_available_models()
model_choices = [(info["name"], key) for key, info in models.items()]
css = """
.model-comparison {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 20px;
border-radius: 10px;
text-align: center;
}
.recommendation-box {
background-color: #f8f9fa;
border: 2px solid #28a745;
border-radius: 8px;
padding: 15px;
margin: 10px 0;
}
"""
with gr.Blocks(css=css, title="OCR Model Comparison Tool") as demo:
gr.HTML("""
<div class="model-comparison">
<h1>🔍 License Plate OCR Model Comparison</h1>
<p>Compare different OCR models on your license plate images</p>
</div>
""")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Input")
input_image = gr.Image(
type="filepath",
label="Upload License Plate Image",
sources=["upload", "webcam"]
)
model_selector = gr.CheckboxGroup(
choices=model_choices,
value=["trocr_license", "easyocr"],
label="Select Models to Compare",
info="Choose which models to test (recommended: start with 2-3 models)"
)
compare_btn = gr.Button("🚀 Compare Models", variant="primary", size="lg")
gr.Markdown("### Available Models")
model_info_text = ""
for key, info in models.items():
model_info_text += f"**{info['name']}** ({info['type']})\n{info['description']}\n\n"
gr.Markdown(model_info_text)
with gr.Column(scale=2):
gr.Markdown("### Comparison Results")
status_output = gr.Textbox(
label="Status",
placeholder="Upload an image and select models to compare...",
interactive=False
)
comparison_table = gr.Dataframe(
label="Detailed Comparison",
headers=["Model", "Type", "Status", "Extracted Text", "Confidence", "Processing Time (s)", "Variants Processed"],
interactive=False
)
with gr.Group(elem_classes="recommendation-box"):
recommendation_output = gr.Markdown(
value="### 🎯 Recommendations will appear here after comparison",
label="Model Recommendation"
)
gr.Markdown("### Quick Start Guide")
gr.Markdown("""
1. **Upload** a license plate image
2. **Select** 2-3 models to compare (recommended combinations):
- `TrOCR License Plates + EasyOCR` (accuracy vs speed)
- `All TrOCR models` (compare TrOCR variants)
- `DETR + YOLO + EasyOCR` (different approaches)
3. **Click Compare** and wait for results
4. **Review** the recommendation for your use case
**Model Types:**
- **Transformers**: Modern AI models (TrOCR) - high accuracy, slower
- **Traditional**: Classic OCR (EasyOCR) - fast, reliable baseline
- **Object Detection**: End-to-end systems (DETR, YOLO) - detect + recognize
""")
compare_btn.click(
fn=compare_ocr_models,
inputs=[input_image, model_selector],
outputs=[status_output, comparison_table, recommendation_output]
)
gr.Examples(
examples=[
[["sample_1.jpg"], ["trocr_license", "easyocr"]],
[["sample_2.jpg"], ["trocr_license", "trocr_base", "easyocr"]]
],
inputs=[input_image, model_selector],
outputs=[status_output, comparison_table, recommendation_output],
fn=compare_ocr_models
)
return demo
if __name__ == "__main__":
demo = create_model_comparison_app()
demo.launch(debug=True, share=True) |