更新模型

.gitignore

@@ -1,2 +1 @@
 /faces
-/models

models/retinaface_mobile320_config.json

@@ -0,0 +1,22 @@
+{
+       "input": {
+         "color": "RGB",
+         "dtype": "float32",
+         "layout": "NCHW",
+         "resize": { "mode": "stretch", "size": [320, 320] }
+       },
+       "decoder": {
+         "type": "retinaface",
+         "steps": [8, 16, 32],
+         "min_sizes": [[16, 32], [64, 128], [256, 512]],
+         "variances": [0.1, 0.2],
+         "score_index": 1,
+         "conf_mode": "auto",
+         "nms_iou_thresh": 0.4
+       },
+       "outputs": {
+         "loc": { "name": "output0" },
+         "conf": { "name": "572" },
+         "landmarks": { "name": "571" }
+       }
+}

testpic.py

@@ -0,0 +1,48 @@
+import os
+import cv2
+import numpy as np
+import onnxruntime as ort
+from gallery_builder.align import align_face_5pts
+from gallery_builder.detector import OnnxFaceDetector, load_det_outputs_config
+
+# ============ 只改这里 ============
+INPUT_IMAGE = "003.jpg"
+# =================================
+
+DET_MODEL = "./models/RetinaFace_mobile320.onnx"
+DET_CONFIG = "./models/retinaface_mobile320_config.json"
+RECOG_MODEL = "./models/mobilefacenet_arcface_prenorm.onnx"
+OUT_DIR = "./debug_output"
+
+os.makedirs(OUT_DIR, exist_ok=True)
+base_name = os.path.splitext(os.path.basename(INPUT_IMAGE))[0]
+
+det_cfg = load_det_outputs_config(DET_CONFIG)
+detector = OnnxFaceDetector(DET_MODEL, det_cfg, score_thresh=0.5, pick_face="largest")
+recog_session = ort.InferenceSession(RECOG_MODEL, providers=["CPUExecutionProvider"])
+
+img_bgr = cv2.imread(INPUT_IMAGE)
+det = detector.detect_one(img_bgr)
+print(f"landmarks5:\n{det.landmarks5}")
+
+img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+aligned = align_face_5pts(img_rgb, det.landmarks5)
+
+# 保存对齐图
+cv2.imwrite(f"{OUT_DIR}/{base_name}_aligned.png", cv2.cvtColor(aligned, cv2.COLOR_RGB2BGR))
+np.save(f"{OUT_DIR}/{base_name}_aligned_rgb.npy", aligned)
+
+# 提取 embedding
+x = (aligned.astype(np.float32) - 127.5) / 128.0
+x = x.transpose(2, 0, 1)[np.newaxis, ...]
+out = recog_session.run(None, {recog_session.get_inputs()[0].name: x})[0]
+emb = out.flatten()
+emb = emb / np.linalg.norm(emb)
+
+np.save(f"{OUT_DIR}/{base_name}_emb.npy", emb)
+
+print(f"\n保存到 {OUT_DIR}/")
+print(f"  {base_name}_aligned.png")
+print(f"  {base_name}_aligned_rgb.npy")
+print(f"  {base_name}_emb.npy")
+print(f"\nembedding[0:8]: {emb[:8]}")