mirror of
https://github.com/deepinsight/insightface.git
synced 2025-12-30 16:12:27 +00:00
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import os
|
|
import sys
|
|
import inspireface as ifac
|
|
|
|
# ++ OPTIONAL ++
|
|
|
|
# Enabling will run all the benchmark tests, which takes time
|
|
ENABLE_BENCHMARK_TEST = True
|
|
|
|
# Enabling will run all the CRUD tests, which will take time
|
|
ENABLE_CRUD_TEST = False
|
|
|
|
# Enabling will run the face search benchmark, which takes time and must be configured with the correct
|
|
# 'LFW_FUNNELED_DIR_PATH' parameter
|
|
ENABLE_SEARCH_BENCHMARK_TEST = True
|
|
|
|
# Enabling will run the LFW dataset precision test, which will take time
|
|
ENABLE_LFW_PRECISION_TEST = False
|
|
|
|
# Testing model name
|
|
TEST_MODEL_NAME = "Pikachu"
|
|
# TEST_MODEL_NAME = "Megatron"
|
|
|
|
# Testing length of face feature
|
|
TEST_MODEL_FACE_FEATURE_LENGTH = 512
|
|
|
|
# Testing face comparison image threshold
|
|
TEST_FACE_COMPARISON_IMAGE_THRESHOLD = 0.45
|
|
|
|
# ++ END OPTIONAL ++
|
|
|
|
# Current project path
|
|
TEST_PROJECT_PATH = os.path.dirname(os.path.abspath(__file__))
|
|
|
|
# Current project path
|
|
CURRENT_PROJECT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|
|
|
# Main project path
|
|
MAIN_PROJECT_PATH = os.path.dirname(CURRENT_PROJECT_PATH)
|
|
|
|
# Model zip path
|
|
MODEL_ZIP_PATH = os.path.join(MAIN_PROJECT_PATH, "test_res/pack/")
|
|
|
|
# Testing model full path
|
|
TEST_MODEL_PATH = os.path.join(MODEL_ZIP_PATH, TEST_MODEL_NAME)
|
|
|
|
# Python test data folder
|
|
PYTHON_TEST_DATA_FOLDER = os.path.join(TEST_PROJECT_PATH, "data/")
|
|
|
|
# Stores some temporary file data generated during testing
|
|
TMP_FOLDER = os.path.join(CURRENT_PROJECT_PATH, "tmp")
|
|
|
|
# Default db file path
|
|
DEFAULT_DB_PATH = os.path.join(TMP_FOLDER, ".E63520A95DD5B3892C56DA38C3B28E551D8173FD")
|
|
|
|
# Create tmp if not exist
|
|
os.makedirs(TMP_FOLDER, exist_ok=True)
|
|
|
|
# lfw_funneled Dataset dir path
|
|
LFW_FUNNELED_DIR_PATH = "/Users/tunm/datasets/lfw_funneled/"
|
|
|
|
# The LFW data predicted by the algorithm is used and cached to save time in the next prediction, and it can be
|
|
# re-predicted by manually deleting it
|
|
LFW_PREDICT_DATA_CACHE_PATH = os.path.join(TMP_FOLDER, "LFW_PRED.npy")
|
|
|
|
assert os.path.exists(LFW_FUNNELED_DIR_PATH), "'LFW_FUNNELED_DIR_PATH' is not found."
|
|
|
|
ifac.launch(TEST_MODEL_PATH) |