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#!/usr/bin/env python3
"""
Test script for Hugging Face Dots-OCR Space API
This script demonstrates how to interact with your deployed Dots-OCR Space
at https://algoryn-dots-ocr-idcard.hf.space
"""
import requests
import json
import time
from pathlib import Path
from typing import Optional, Dict, Any, List
class HFDotsOCRClient:
"""Client for interacting with the Hugging Face Dots-OCR Space API."""
def __init__(self, base_url: str = "https://algoryn-dots-ocr-idcard.hf.space"):
"""Initialize the client with the Space URL."""
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
# Set a reasonable timeout for HF Spaces
self.session.timeout = 60
def health_check(self) -> Dict[str, Any]:
"""Check if the Space is healthy and running."""
try:
response = self.session.get(f"{self.base_url}/health")
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
return {"error": f"Health check failed: {e}"}
def extract_text(
self,
image_path: str,
roi: Optional[Dict[str, float]] = None
) -> Dict[str, Any]:
"""Extract text from an identity document image.
Args:
image_path: Path to the image file
roi: Optional region of interest as dict with x1, y1, x2, y2 (0-1 normalized)
"""
try:
with open(image_path, 'rb') as f:
files = {'file': f}
data = {}
# Add ROI if provided
if roi:
data['roi'] = json.dumps(roi)
response = self.session.post(
f"{self.base_url}/v1/id/ocr",
files=files,
data=data
)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
return {"error": f"OCR extraction failed: {e}"}
except FileNotFoundError:
return {"error": f"Image file not found: {image_path}"}
def print_ocr_results(result: Dict[str, Any]) -> None:
"""Pretty print OCR extraction results."""
if "error" in result:
print(f"❌ Error: {result['error']}")
return
print(f"✅ Request ID: {result.get('request_id', 'N/A')}")
print(f"📊 Media Type: {result.get('media_type', 'N/A')}")
print(f"⏱️ Processing Time: {result.get('processing_time', 0):.2f}s")
detections = result.get('detections', [])
print(f"🔍 OCR Detections: {len(detections)}")
for i, detection in enumerate(detections, 1):
print(f"\n📄 Detection {i}:")
# Print MRZ data if available
mrz_data = detection.get('mrz_data')
if mrz_data:
print(f" 🆔 MRZ Data:")
print(f" Format: {mrz_data.get('format_type', 'N/A')}")
print(f" Valid: {mrz_data.get('is_valid', False)}")
print(f" Confidence: {mrz_data.get('confidence', 0):.3f}")
if mrz_data.get('raw_text'):
print(f" Raw Text: {mrz_data['raw_text'][:50]}...")
else:
print(f" 🆔 MRZ Data: None detected")
# Print extracted fields
extracted_fields = detection.get('extracted_fields', {})
print(f" 📋 Extracted Fields:")
# Define field categories for better organization
field_categories = {
"Document Info": [
"document_number", "document_type", "issuing_country", "issuing_authority"
],
"Personal Info": [
"surname", "given_names", "nationality", "date_of_birth",
"gender", "place_of_birth"
],
"Validity Info": [
"date_of_issue", "date_of_expiry", "personal_number"
],
"Additional": [
"optional_data_1", "optional_data_2"
]
}
for category, fields in field_categories.items():
category_fields = []
for field_name in fields:
field_data = extracted_fields.get(field_name)
if field_data and field_data.get('value'):
category_fields.append(f"{field_name}: {field_data['value']} ({field_data.get('confidence', 0):.2f})")
if category_fields:
print(f" {category}:")
for field in category_fields:
print(f" • {field}")
def test_with_roi(client: HFDotsOCRClient, image_path: str) -> None:
"""Test OCR with different ROI regions."""
print(f"\n🎯 Testing with different ROI regions...")
# Define different ROI regions to test
roi_regions = {
"Full Image": None,
"Top Half": {"x1": 0.0, "y1": 0.0, "x2": 1.0, "y2": 0.5},
"Bottom Half": {"x1": 0.0, "y1": 0.5, "x2": 1.0, "y2": 1.0},
"Center Region": {"x1": 0.25, "y1": 0.25, "x2": 0.75, "y2": 0.75},
"Left Side": {"x1": 0.0, "y1": 0.0, "x2": 0.5, "y2": 1.0},
"Right Side": {"x1": 0.5, "y1": 0.0, "x2": 1.0, "y2": 1.0}
}
for region_name, roi in roi_regions.items():
print(f"\n📍 Testing {region_name}...")
result = client.extract_text(image_path, roi)
if "error" not in result:
print(f" ✅ Success - Processing time: {result.get('processing_time', 0):.2f}s")
# Show a summary of extracted fields
detections = result.get('detections', [])
if detections:
fields = detections[0].get('extracted_fields', {})
field_count = sum(1 for field in fields.values() if field and field.get('value'))
print(f" 📊 Extracted {field_count} fields")
else:
print(f" ❌ Error: {result['error']}")
def main():
"""Main test function."""
print("🚀 Testing Hugging Face Dots-OCR Space API")
print("=" * 50)
# Initialize client
client = HFDotsOCRClient()
# Test 1: Health check
print("\n1️⃣ Testing health check...")
health = client.health_check()
if "error" in health:
print(f"❌ Health check failed: {health['error']}")
print("💡 Make sure your Hugging Face Space is running and accessible")
return
else:
print(f"✅ Space is healthy: {health}")
# Test 2: OCR extraction (if demo images exist)
demo_images = [
"data/demo/tom_id_card_front.jpg",
"data/demo/tom_id_card_back.jpg",
"data/demo/ocr/0000095097_1_E-5858-MA Fahrzeugschein und -brief.png",
"data/demo/ocr/container_inspection_report.png",
"data/demo/ocr/handelsregister_b.png"
]
test_image = None
for image_path in demo_images:
if Path(image_path).exists():
test_image = image_path
break
if test_image:
print(f"\n2️⃣ Testing OCR extraction with {test_image}...")
result = client.extract_text(test_image)
print_ocr_results(result)
# Test 3: ROI testing
if "error" not in result:
test_with_roi(client, test_image)
else:
print("\n2️⃣ No demo images found for testing")
print("💡 Place some test images in the data/demo/ directory")
print("\n🎉 Testing complete!")
print("\n💡 To test with your own files:")
print(" python test_hf_dots_ocr_space.py")
print("\n💡 To test with ROI:")
print(" client = HFDotsOCRClient()")
print(" result = client.extract_text('image.jpg', roi={'x1': 0.0, 'y1': 0.0, 'x2': 0.5, 'y2': 0.5})")
if __name__ == "__main__":
main()
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