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#!/usr/bin/env python3
"""
Test script to verify the video processing fix works correctly.
This script tests the predict_actions function with different scenarios.
"""
import sys
import tempfile
from pathlib import Path
import logging
# Configure logging to see debug output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
try:
from predict import predict_actions, _read_video_frames, load_model
print("✓ Successfully imported predict functions")
except ImportError as e:
print(f"✗ Failed to import predict functions: {e}")
sys.exit(1)
def create_test_video(output_path: Path, duration: int = 2, fps: int = 10):
"""Create a simple test video using OpenCV."""
try:
import cv2
import numpy as np
except ImportError:
print("OpenCV not available for creating test video")
return False
# Create a simple test video with moving rectangle
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(str(output_path), fourcc, fps, (224, 224))
total_frames = duration * fps
for i in range(total_frames):
# Create frame with moving rectangle
frame = np.zeros((224, 224, 3), dtype=np.uint8)
x_pos = int(50 + 100 * (i / total_frames))
cv2.rectangle(frame, (x_pos, 50), (x_pos + 50, 150), (0, 255, 0), -1)
out.write(frame)
out.release()
return True
def test_frame_reading(video_path: Path):
"""Test frame reading functionality."""
print(f"\n--- Testing frame reading from {video_path.name} ---")
try:
frames = _read_video_frames(video_path, num_frames=8)
print(f"✓ Successfully read {len(frames)} frames")
# Check frame properties
if frames:
frame = frames[0]
print(f"✓ Frame size: {frame.size}")
print(f"✓ Frame mode: {frame.mode}")
# Check all frames have same size
sizes = [f.size for f in frames]
if len(set(sizes)) == 1:
print("✓ All frames have consistent size")
else:
print(f"⚠ Inconsistent frame sizes: {set(sizes)}")
return True
except Exception as e:
print(f"✗ Frame reading failed: {e}")
return False
def test_model_loading():
"""Test model loading functionality."""
print("\n--- Testing model loading ---")
try:
processor, model, device = load_model()
print(f"✓ Successfully loaded model on device: {device}")
print(f"✓ Model config num_frames: {getattr(model.config, 'num_frames', 'Not specified')}")
return True, (processor, model, device)
except Exception as e:
print(f"✗ Model loading failed: {e}")
return False, (None, None, None)
def test_prediction(video_path: Path):
"""Test full prediction pipeline."""
print(f"\n--- Testing prediction on {video_path.name} ---")
try:
predictions = predict_actions(str(video_path), top_k=3)
print(f"✓ Successfully got {len(predictions)} predictions")
for i, (label, score) in enumerate(predictions, 1):
print(f" {i}. {label}: {score:.4f} ({score*100:.2f}%)")
return True
except Exception as e:
print(f"✗ Prediction failed: {e}")
import traceback
traceback.print_exc()
return False
def main():
print("🧪 Starting Video Action Recognition Test Suite")
# Test 1: Model loading
model_loaded, _ = test_model_loading()
if not model_loaded:
print("❌ Model loading failed - cannot continue tests")
return
# Test 2: Create test video
with tempfile.TemporaryDirectory() as tmp_dir:
test_video_path = Path(tmp_dir) / "test_video.mp4"
print(f"\n--- Creating test video at {test_video_path} ---")
if create_test_video(test_video_path):
print("✓ Test video created successfully")
# Test 3: Frame reading
if test_frame_reading(test_video_path):
print("✓ Frame reading test passed")
else:
print("❌ Frame reading test failed")
return
# Test 4: Full prediction
if test_prediction(test_video_path):
print("✅ All tests passed! The fix is working correctly.")
else:
print("❌ Prediction test failed")
else:
print("⚠ Could not create test video, skipping video-based tests")
print("💡 Try testing with an existing video file")
if __name__ == "__main__":
main()
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