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69def8e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import argparse
import csv
import os
import time
import sys
import numpy as np
import yaml
# Add workspace to path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src.similarity import numpy_vectorized_cosine
from src.evaluation import compute_confidence
REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
DEFAULT_CONFIG_PATH = os.path.join(REPO_ROOT, 'configs', 'inference_config.yaml')
def _load_embedder_class():
# Lazy import keeps CLI helper paths lightweight and test-friendly.
from src.embedding import FaceEmbedder
return FaceEmbedder
def load_inference_config(config_path):
defaults = {
'model_name': 'Facenet',
'threshold': 0.35,
'confidence_k': 10.0,
'score_is_distance': False,
}
if config_path is None or not os.path.exists(config_path):
return defaults
with open(config_path, 'r') as f:
loaded = yaml.safe_load(f) or {}
config = defaults.copy()
config.update(loaded)
config['threshold'] = float(config['threshold'])
config['confidence_k'] = float(config['confidence_k'])
config['score_is_distance'] = bool(config['score_is_distance'])
config['model_name'] = str(config['model_name'])
return config
def run_inference(
img1_path,
img2_path,
threshold=0.35,
model_name='Facenet',
confidence_k=10.0,
score_is_distance=False,
embedder=None,
):
"""
Run the full inference pipeline for one pair of images.
Returns a dictionary with result details.
"""
start_total = time.perf_counter()
if embedder is None:
FaceEmbedder = _load_embedder_class()
embedder = FaceEmbedder(model_name=model_name)
# 1. Preprocessing & Embedding Extraction
start_emb = time.perf_counter()
emb1 = embedder.compute_embedding(img1_path)
emb2 = embedder.compute_embedding(img2_path)
latency_emb = time.perf_counter() - start_emb
# 2. Similarity Scoring
start_scoring = time.perf_counter()
# Reshape for vectorized function
score = numpy_vectorized_cosine(emb1.reshape(1, -1), emb2.reshape(1, -1))[0]
latency_scoring = time.perf_counter() - start_scoring
# 3. Decision
if score_is_distance:
is_same = score < threshold
else:
is_same = score >= threshold
decision = "SAME" if is_same else "DIFFERENT"
# 4. Calibrated Confidence
confidence = compute_confidence(
score,
threshold,
k=confidence_k,
score_is_distance=score_is_distance,
)
latency_total = time.perf_counter() - start_total
return {
"img1": img1_path,
"img2": img2_path,
"similarity_score": round(float(score), 4),
"threshold": threshold,
"decision": decision,
"is_same": int(is_same),
"confidence": round(confidence, 4),
"model_name": model_name,
"latency_total_ms": round(latency_total * 1000, 2),
"latency_emb_ms": round(latency_emb * 1000, 2),
"latency_scoring_ms": round(latency_scoring * 1000, 2)
}
def _resolve_runtime_config(args):
config = load_inference_config(args.config)
if args.threshold is not None:
config['threshold'] = float(args.threshold)
if args.model_name is not None:
config['model_name'] = str(args.model_name)
return config
def main():
parser = argparse.ArgumentParser(description="FaceID Inference CLI (Milestone 4)")
parser.add_argument(
"--config",
type=str,
default=None,
help="Path to inference YAML config",
)
parser.add_argument("--img1", type=str, help="Path to first face image")
parser.add_argument("--img2", type=str, help="Path to second face image")
parser.add_argument(
"--threshold",
type=float,
default=None,
help="Optional threshold override. Default: 0.35",
)
parser.add_argument(
"--model-name",
type=str,
default=None,
help="Optional embedding model override.",
)
parser.add_argument("--batch", type=str, help="Path to batch CSV file (with left_path, right_path)")
args = parser.parse_args()
runtime_config = _resolve_runtime_config(args)
FaceEmbedder = _load_embedder_class()
embedder = FaceEmbedder(model_name=runtime_config['model_name'])
if args.batch:
if not os.path.exists(args.batch):
print(f"Error: Batch file {args.batch} not found.")
return
print(f"{'Img1':<40} | {'Img2':<40} | {'Score':<8} | {'Decision':<10} | {'Conf':<6} | {'Lat(ms)':<8}")
print("-" * 130)
with open(args.batch, 'r') as f:
reader = csv.DictReader(f)
for row in reader:
res = run_inference(
row['left_path'],
row['right_path'],
threshold=runtime_config['threshold'],
model_name=runtime_config['model_name'],
confidence_k=runtime_config.get('confidence_k', 10.0),
score_is_distance=runtime_config.get('score_is_distance', False),
embedder=embedder,
)
print(f"{os.path.basename(res['img1']):<40} | {os.path.basename(res['img2']):<40} | {res['similarity_score']:<8.4f} | {res['decision']:<10} | {res['confidence']:<6.4f} | {res['latency_total_ms']:<8.2f}")
elif args.img1 and args.img2:
res = run_inference(
args.img1,
args.img2,
threshold=runtime_config['threshold'],
model_name=runtime_config['model_name'],
confidence_k=runtime_config.get('confidence_k', 10.0),
score_is_distance=runtime_config.get('score_is_distance', False),
embedder=embedder,
)
print("\n--- FaceID Inference Result ---")
print(f"Inputs: {res['img1']} vs {res['img2']}")
print(f"Score: {res['similarity_score']:.4f} (Threshold: {res['threshold']})")
print(f"Decision: {res['decision']}")
print(f"Confidence: {res['confidence']:.4f}")
print(f"Latency: Total: {res['latency_total_ms']}ms (Emb: {res['latency_emb_ms']}ms, Scoring: {res['latency_scoring_ms']}ms)")
print("-------------------------------\n")
else:
parser.print_help()
if __name__ == "__main__":
main()
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