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887f5f0 | 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 | import os
import cv2
import numpy as np
from scripts.deepfake_classifier import DeepfakeClassifier
from scripts.pipeline import DetectionPipeline
def generate_mock_face_image(path: str):
"""
Generates a mock face-like image (concentric circles for eyes/face)
and saves it to test pipeline functionality.
"""
# Create black canvas
img = np.zeros((300, 300, 3), dtype=np.uint8)
# Draw face boundary (circle)
cv2.circle(img, (150, 150), 120, (200, 200, 200), -1) # Light gray face
# Draw left eye
cv2.circle(img, (100, 120), 15, (50, 50, 50), -1)
# Draw right eye
cv2.circle(img, (200, 120), 15, (50, 50, 50), -1)
# Draw mouth
cv2.ellipse(img, (150, 180), (40, 20), 0, 0, 180, (50, 50, 50), -1)
# Save image
cv2.imwrite(path, img)
print(f"Generated mock face image at: {path}")
def run_test():
print("=== Deepfake Detection System - Pipeline Test ===")
test_img_path = "test_target_face.jpg"
# 1. Create a dummy image
generate_mock_face_image(test_img_path)
# 2. Instantiate services
print("\n[1/3] Initializing Deepfake Classifier...")
classifier = DeepfakeClassifier(use_gpu=False) # Disable GPU for test speed/compatibility
print("\n[2/3] Initializing Detection Pipeline...")
pipeline = DetectionPipeline(classifier)
# 3. Analyze mock image
print("\n[3/3] Analyzing media...")
try:
report = pipeline.analyze_media(test_img_path, is_image=True)
print("\n=== Analysis Report ===")
print(f"Filename: {report['filename']}")
print(f"File Hash: {report['file_hash']}")
print(f"Is Image: {report['is_image']}")
print(f"Global Fake Score: {report['global_fake_score']} ({report['global_fake_score']*100:.1f}%)")
print(f"Verdict: {'DEEPFAKE' if report['is_fake'] else 'AUTHENTIC'}")
print(f"Confidence: {report['confidence']*100:.1f}%")
print(f"Faces Detected: {report['total_faces_detected']}")
print(f"Used ViT Model: {report['used_vit_model']}")
print(f"Processing Time: {report['processing_time_sec']} seconds")
# Print heuristics info
print("\nHeuristics Breakdowns:")
for k, v in report['average_heuristics'].items():
print(f" - {k}: {v}")
print("\nFrames Analyzed Detail:")
for frame in report['frames']:
print(f" - Frame #{frame['frame_idx']} contains {len(frame['faces'])} face(s)")
for idx, face in enumerate(frame['faces']):
print(f" * Face #{idx+1} score: {face['fake_score']} | has crop preview (length: {len(face['crop_b64']) if face['crop_b64'] else 0} chars)")
print("\nTest completed successfully!")
except Exception as e:
print(f"\nTest failed with error: {e}")
raise e
finally:
# Cleanup
if os.path.exists(test_img_path):
os.remove(test_img_path)
print(f"Removed temporary test file: {test_img_path}")
if __name__ == "__main__":
run_test()
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