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Update inspireface to 1.2.0

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Jingyu
2025-03-25 00:51:26 +08:00
parent 977ea6795b
commit ca64996b84
388 changed files with 28584 additions and 13036 deletions
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52
cpp-package/inspireface/python/sample_face_recognition.py
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@@ -1,36 +1,34 @@
import os
import cv2
import inspireface as ifac
from inspireface.param import *
import inspireface as isf
import click
@click.command()
@click.argument("resource_path")
@click.argument('test_data_folder')
def case_face_recognition(resource_path, test_data_folder):
def case_face_recognition(test_data_folder):
"""
Launches the face recognition system, inserts face features into a database, and performs searches.
Args:
resource_path (str): Path to the resource directory for face recognition algorithms.
test_data_folder (str): Path to the test data containing images for insertion and recognition tests.
"""
# Initialize the face recognition system with provided resources.
ret = ifac.launch(resource_path)
# If you need to switch from the default Pikachu model to another model like Megatron, you can use reload
ret = isf.reload("Megatron")
assert ret, "Launch failure. Please ensure the resource path is correct."
# Enable face recognition features.
opt = HF_ENABLE_FACE_RECOGNITION
session = ifac.InspireFaceSession(opt, HF_DETECT_MODE_ALWAYS_DETECT)
opt = isf.HF_ENABLE_FACE_RECOGNITION
session = isf.InspireFaceSession(opt, isf.HF_DETECT_MODE_ALWAYS_DETECT)
# Configure the feature management system.
feature_hub_config = ifac.FeatureHubConfiguration(
feature_block_num=10,
enable_use_db=False,
db_path="",
feature_hub_config = isf.FeatureHubConfiguration(
primary_key_mode=isf.HF_PK_AUTO_INCREMENT,
enable_persistence=False,
persistence_db_path="",
search_threshold=0.48,
search_mode=HF_SEARCH_MODE_EAGER,
search_mode=isf.HF_SEARCH_MODE_EAGER,
)
ret = ifac.feature_hub_enable(feature_hub_config)
ret = isf.feature_hub_enable(feature_hub_config)
assert ret, "Failed to enable FeatureHub."
# Insert face features from 'bulk' directory.
@@ -46,11 +44,11 @@ def case_face_recognition(resource_path, test_data_folder):
if faces:
face = faces[0] # Assume the most prominent face is what we want.
feature = session.face_feature_extract(image, face)
identity = ifac.FaceIdentity(feature, custom_id=idx, tag=name)
ret = ifac.feature_hub_face_insert(identity)
identity = isf.FaceIdentity(feature, id=idx)
ret, alloc_id = isf.feature_hub_face_insert(identity)
assert ret, "Failed to insert face."
count = ifac.feature_hub_get_face_count()
count = isf.feature_hub_get_face_count()
print(f"Number of faces inserted: {count}")
# Process faces from 'RD' directory and insert them.
@@ -66,11 +64,11 @@ def case_face_recognition(resource_path, test_data_folder):
if faces:
face = faces[0]
feature = session.face_feature_extract(image, face)
identity = ifac.FaceIdentity(feature, custom_id=idx+count+1, tag=name)
ret = ifac.feature_hub_face_insert(identity)
identity = isf.FaceIdentity(feature, id=idx+count+1)
ret, alloc_id = isf.feature_hub_face_insert(identity)
assert ret, "Failed to insert face."
count = ifac.feature_hub_get_face_count()
count = isf.feature_hub_get_face_count()
print(f"Total number of faces after insertion: {count}")
# Search for a similar face using the last image in RD directory.
@@ -81,18 +79,18 @@ def case_face_recognition(resource_path, test_data_folder):
face = faces[0]
feature = session.face_feature_extract(remain, face)
search = ifac.feature_hub_face_search(feature)
if search.similar_identity.custom_id != -1:
print(f"Found similar identity with ID: {search.similar_identity.custom_id}, Tag: {search.similar_identity.tag}, Confidence: {search.confidence:.2f}")
search = isf.feature_hub_face_search(feature)
if search.similar_identity.id != -1:
print(f"Found similar identity with ID: {search.similar_identity.id}, Confidence: {search.confidence:.2f}")
else:
print("No similar identity found.")
# Display top-k similar face identities.
print("Top-k similar identities:")
search_top_k = ifac.feature_hub_face_search_top_k(feature, 10)
for idx, (conf, custom_id) in enumerate(search_top_k):
identity = ifac.feature_hub_get_face_identity(custom_id)
print(f"Top-{idx + 1}: {identity.tag}, ID: {custom_id}, Confidence: {conf:.2f}")
search_top_k = isf.feature_hub_face_search_top_k(feature, 10)
for idx, (conf, _id) in enumerate(search_top_k):
identity = isf.feature_hub_get_face_identity(_id)
print(f"Top-{idx + 1}: ID: {_id}, Confidence: {conf:.2f}")
if __name__ == '__main__':