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"""
Simple test script to verify Transformers pipeline integration
"""
from transformers import pipeline
import torch
from PIL import Image
import numpy as np
def test_transformers_pipeline():
"""Test if transformers depth estimation pipeline works"""
print("🧪 Testing Transformers Depth Estimation Pipeline")
print("=" * 50)
try:
# Initialize pipeline
print("1. Initializing pipeline...")
pipe = pipeline(
"depth-estimation",
model="apple/DepthPro",
device=-1, # CPU
torch_dtype=torch.float32
)
print("✅ Pipeline initialized successfully!")
# Create test image
print("2. Creating test image...")
test_image = Image.new('RGB', (640, 480), color='blue')
# Add some pattern
pixels = np.array(test_image)
for y in range(480):
for x in range(640):
intensity = int(255 * (1 - y / 480))
pixels[y, x] = [intensity, intensity//2, intensity//3]
test_image = Image.fromarray(pixels.astype(np.uint8))
print("✅ Test image created!")
# Test pipeline
print("3. Running depth estimation...")
result = pipe(test_image)
print("✅ Pipeline executed successfully!")
# Check result
print("4. Checking results...")
if isinstance(result, dict):
if 'depth' in result:
depth = result['depth']
print(f" Depth type: {type(depth)}")
if hasattr(depth, 'shape'):
print(f" Depth shape: {depth.shape}")
elif hasattr(depth, 'size'):
print(f" Depth size: {depth.size}")
print("✅ Valid depth result obtained!")
return True
else:
print(f" Result keys: {result.keys()}")
print("⚠️ No 'depth' key in result")
return False
else:
print(f" Result type: {type(result)}")
if hasattr(result, 'depth'):
print("✅ Result has depth attribute!")
return True
else:
print("⚠️ Result format unexpected")
return False
except ImportError as e:
print(f"❌ Import error: {e}")
print("💡 Try: pip install transformers torch")
return False
except Exception as e:
print(f"❌ Pipeline test failed: {e}")
print("💡 This is expected if the model isn't available or if there are compatibility issues")
return False
def test_fallback_dummy():
"""Test the dummy pipeline fallback"""
print("\n🧪 Testing Dummy Pipeline Fallback")
print("=" * 40)
try:
# Import dummy pipeline from our app
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from app import DummyDepthPipeline
dummy = DummyDepthPipeline()
test_image = Image.new('RGB', (512, 384), color='green')
result = dummy(test_image)
if isinstance(result, dict) and 'depth' in result:
depth = result['depth']
print(f"✅ Dummy pipeline works! Depth shape: {depth.shape}")
print(f" Depth range: {np.min(depth):.2f} - {np.max(depth):.2f}")
return True
else:
print(f"❌ Unexpected result format: {type(result)}")
return False
except Exception as e:
print(f"❌ Dummy pipeline test failed: {e}")
return False
if __name__ == "__main__":
print("🚀 Testing Depth Pro Transformers Integration\n")
# Test real pipeline
pipeline_works = test_transformers_pipeline()
# Test fallback
dummy_works = test_fallback_dummy()
print("\n" + "="*50)
print("🏁 Test Summary:")
print("="*50)
print(f"Transformers Pipeline: {'✅ WORKS' if pipeline_works else '❌ FAILED (expected in some environments)'}")
print(f"Dummy Pipeline Fallback: {'✅ WORKS' if dummy_works else '❌ FAILED'}")
if dummy_works:
print("\n🎉 The app should work with fallback even if the real model fails!")
else:
print("\n⚠️ There may be issues with the fallback implementation.")
if pipeline_works:
print("🌟 Real Depth Pro model should work perfectly!")
else:
print("💡 Real model may need specific environment setup or GPU.")
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