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#!/usr/bin/env python3
"""
Test script to verify video processing functionality.
Creates a synthetic test video and tests the prediction pipeline.
"""
import sys
import tempfile
import logging
from pathlib import Path
import numpy as np
from PIL import Image, ImageDraw
import cv2
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def create_synthetic_video(output_path: Path, duration_seconds: float = 2.0, fps: int = 24):
"""Create a synthetic test video with simple animation."""
width, height = 640, 480
total_frames = int(duration_seconds * fps)
# Create video writer
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height))
logging.info(f"Creating synthetic video: {total_frames} frames at {fps} FPS")
for frame_num in range(total_frames):
# Create a frame with animated content
frame = np.zeros((height, width, 3), dtype=np.uint8)
# Add background gradient
for y in range(height):
intensity = int(255 * (y / height))
frame[y, :] = [intensity // 3, intensity // 2, intensity]
# Add moving circle (simulating an action)
center_x = int(width * (0.2 + 0.6 * frame_num / total_frames))
center_y = height // 2
radius = 30 + int(20 * np.sin(frame_num * 0.3))
# Convert to PIL for drawing
pil_frame = Image.fromarray(frame)
draw = ImageDraw.Draw(pil_frame)
# Draw moving circle
left = center_x - radius
top = center_y - radius
right = center_x + radius
bottom = center_y + radius
draw.ellipse([left, top, right, bottom], fill=(255, 255, 0))
# Add some text to simulate action
draw.text((50, 50), f"Frame {frame_num}", fill=(255, 255, 255))
draw.text((50, 80), "Synthetic Action", fill=(255, 255, 255))
# Convert back to numpy and BGR for OpenCV
frame = np.array(pil_frame)
frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
out.write(frame_bgr)
out.release()
logging.info(f"✓ Created synthetic video: {output_path}")
return output_path
def test_video_reading():
"""Test video reading functionality without full model inference."""
logging.info("=== Testing Video Reading ===")
try:
from predict import _read_video_frames, normalize_frames
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_path = Path(tmp_dir)
video_path = tmp_path / "test_video.mp4"
# Create test video
create_synthetic_video(video_path, duration_seconds=1.0, fps=12) # Short video
# Test reading frames
logging.info("Testing frame reading...")
frames = _read_video_frames(video_path, num_frames=8)
if not frames:
logging.error("✗ No frames extracted")
return False
logging.info(f"✓ Extracted {len(frames)} frames")
# Test frame normalization
logging.info("Testing frame normalization...")
normalized = normalize_frames(frames, required_frames=8)
if len(normalized) != 8:
logging.error(f"✗ Expected 8 frames, got {len(normalized)}")
return False
logging.info("✓ Frame normalization successful")
# Check frame properties
for i, frame in enumerate(normalized):
if frame.size != (224, 224):
logging.error(f"✗ Frame {i} has wrong size: {frame.size}")
return False
if frame.mode != 'RGB':
logging.error(f"✗ Frame {i} has wrong mode: {frame.mode}")
return False
logging.info("✓ All frames have correct properties")
return True
except Exception as e:
logging.error(f"✗ Video reading test failed: {e}")
return False
def test_tensor_creation():
"""Test tensor creation from frames."""
logging.info("=== Testing Tensor Creation ===")
try:
from predict import create_tensor_from_frames
import torch
# Create dummy frames
frames = []
for i in range(8):
frame = Image.new('RGB', (224, 224), (i*30 % 255, 100, 150))
frames.append(frame)
logging.info("Testing tensor creation...")
tensor = create_tensor_from_frames(frames, processor=None) # Use manual creation
# Check tensor properties
expected_shape = (1, 3, 8, 224, 224) # (batch, channels, frames, height, width)
if tensor.shape != expected_shape:
logging.error(f"✗ Expected shape {expected_shape}, got {tensor.shape}")
return False
logging.info(f"✓ Tensor created with correct shape: {tensor.shape}")
# Check tensor values are in reasonable range
if tensor.min() < 0 or tensor.max() > 1:
logging.warning(f"⚠ Tensor values outside [0,1]: [{tensor.min():.3f}, {tensor.max():.3f}]")
logging.info("✓ Tensor creation successful")
return True
except Exception as e:
logging.error(f"✗ Tensor creation test failed: {e}")
return False
def test_full_pipeline():
"""Test the complete prediction pipeline with a synthetic video."""
logging.info("=== Testing Full Pipeline ===")
try:
from predict import predict_actions
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_path = Path(tmp_dir)
video_path = tmp_path / "test_video.mp4"
# Create test video
create_synthetic_video(video_path, duration_seconds=2.0, fps=15)
logging.info("Running full prediction pipeline...")
# Run prediction with smaller top_k for faster testing
results = predict_actions(str(video_path), top_k=3)
if not results:
logging.error("✗ No predictions returned")
return False
logging.info(f"✓ Got {len(results)} predictions")
# Display results
for i, (label, confidence) in enumerate(results, 1):
logging.info(f" {i}. {label}: {confidence:.3f}")
# Basic validation
if len(results) != 3:
logging.error(f"✗ Expected 3 results, got {len(results)}")
return False
for label, confidence in results:
if not isinstance(label, str) or not isinstance(confidence, float):
logging.error(f"✗ Invalid result format: {label}, {confidence}")
return False
if confidence < 0 or confidence > 1:
logging.error(f"✗ Invalid confidence: {confidence}")
return False
logging.info("✓ Full pipeline test successful")
return True
except Exception as e:
logging.error(f"✗ Full pipeline test failed: {e}")
logging.exception("Full error traceback:")
return False
def main():
"""Run all tests."""
print("🧪 Video Processing Test Suite")
print("=" * 50)
tests = [
("Video Reading", test_video_reading),
("Tensor Creation", test_tensor_creation),
("Full Pipeline", test_full_pipeline),
]
passed = 0
total = len(tests)
for test_name, test_func in tests:
print(f"\n🔍 Running: {test_name}")
print("-" * 30)
try:
if test_func():
print(f"✅ {test_name} PASSED")
passed += 1
else:
print(f"❌ {test_name} FAILED")
except Exception as e:
print(f"💥 {test_name} CRASHED: {e}")
logging.exception(f"Test {test_name} crashed:")
print(f"\n📊 Test Results: {passed}/{total} tests passed")
if passed == total:
print("🎉 All tests passed! Video processing is working correctly.")
return 0
else:
print("⚠️ Some tests failed. Check the logs above for details.")
return 1
if __name__ == "__main__":
exit(main())
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