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1f4e4ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | import cv2
import numpy as np
import os
import sys
from sign_utils import MediaPipeLandmarksModel, SignLanguagePredictor
def test_camera():
"""Test if camera is working."""
print("Testing camera...")
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("❌ Camera not accessible")
return False
ret, frame = cap.read()
if not ret:
print("❌ Cannot read from camera")
cap.release()
return False
print("✅ Camera working")
cap.release()
return True
def test_mediapipe():
"""Test MediaPipe hand detection."""
print("Testing MediaPipe...")
try:
landmark_model = MediaPipeLandmarksModel()
# Create a dummy image
dummy_image = np.zeros((480, 640, 3), dtype=np.uint8)
landmarks = landmark_model.predict_landmarks(dummy_image)
print("✅ MediaPipe working")
return True
except Exception as e:
print(f"❌ MediaPipe error: {e}")
return False
def test_model_files():
"""Test if model files exist."""
print("Testing model files...")
model_path = "models/model.tflite"
labels_path = "models/labels.txt"
if not os.path.exists(model_path):
print(f"❌ Model file not found: {model_path}")
return False
if not os.path.exists(labels_path):
print(f"❌ Labels file not found: {labels_path}")
return False
print("✅ Model files found")
return True
def test_predictor():
"""Test the sign language predictor."""
print("Testing predictor...")
if not test_model_files():
return False
try:
predictor = SignLanguagePredictor(
model_path="models/model.tflite",
labels_path="models/labels.txt"
)
# Create a dummy image
dummy_image = np.zeros((480, 640, 3), dtype=np.uint8)
prediction, confidence = predictor.predict(dummy_image)
print(f"✅ Predictor working - Sample prediction: {prediction} ({confidence:.2f})")
return True
except Exception as e:
print(f"❌ Predictor error: {e}")
return False
def test_dependencies():
"""Test if all dependencies are installed."""
print("Testing dependencies...")
required_modules = [
'cv2', 'numpy', 'tensorflow', 'mediapipe',
'streamlit', 'sklearn'
]
missing_modules = []
for module in required_modules:
try:
__import__(module)
except ImportError:
missing_modules.append(module)
if missing_modules:
print(f"❌ Missing modules: {missing_modules}")
return False
print("✅ All dependencies installed")
return True
def run_full_test():
"""Run complete system test."""
print("🤟 Sign Language Interpreter - System Test")
print("=" * 50)
tests = [
("Dependencies", test_dependencies),
("Camera", test_camera),
("MediaPipe", test_mediapipe),
("Model Files", test_model_files),
("Predictor", test_predictor)
]
passed = 0
total = len(tests)
for test_name, test_func in tests:
print(f"\n{test_name}:")
if test_func():
passed += 1
else:
print(f" Fix this issue before proceeding")
print(f"\n{'='*50}")
print(f"Test Results: {passed}/{total} tests passed")
if passed == total:
print("🎉 All tests passed! System is ready to use.")
print("\nNext steps:")
print("1. Run 'streamlit run app.py' to start the application")
print("2. Or run 'python data_collection.py' to collect custom data")
else:
print("❌ Some tests failed. Please fix the issues above.")
if not test_model_files():
print("\n💡 Tip: Run 'python create_sample_model.py' to create a sample model")
def main():
if len(sys.argv) > 1 and sys.argv[1] == "--quick":
# Quick test - just check if basic components work
if test_dependencies() and test_mediapipe():
print("✅ Quick test passed!")
else:
print("❌ Quick test failed!")
else:
run_full_test()
if __name__ == "__main__":
main()
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