mirror of
https://github.com/serengil/deepface.git
synced 2025-06-04 02:20:06 +00:00
294 lines
10 KiB
Python
294 lines
10 KiB
Python
# 3rd party dependencies
|
||
import cv2
|
||
import numpy as np
|
||
|
||
# project dependencies
|
||
from deepface import DeepFace
|
||
from deepface.models.demography import Age, Emotion, Gender, Race
|
||
from deepface.commons.logger import Logger
|
||
|
||
logger = Logger()
|
||
|
||
|
||
detectors = ["opencv", "mtcnn"]
|
||
|
||
|
||
def test_standard_analyze():
|
||
img = "dataset/img4.jpg"
|
||
demography_objs = DeepFace.analyze(img, silent=True)
|
||
|
||
# return type should be list of dict for non batch input
|
||
assert isinstance(demography_objs, list)
|
||
|
||
for demography in demography_objs:
|
||
assert isinstance(demography, dict)
|
||
logger.debug(demography)
|
||
assert demography["age"] > 20 and demography["age"] < 40
|
||
assert demography["dominant_gender"] == "Woman"
|
||
logger.info("✅ test standard analyze done")
|
||
|
||
|
||
def test_analyze_with_all_actions_as_tuple():
|
||
img = "dataset/img4.jpg"
|
||
demography_objs = DeepFace.analyze(
|
||
img, actions=("age", "gender", "race", "emotion"), silent=True
|
||
)
|
||
|
||
for demography in demography_objs:
|
||
logger.debug(f"Demography: {demography}")
|
||
assert type(demography) == dict
|
||
age = demography["age"]
|
||
gender = demography["dominant_gender"]
|
||
race = demography["dominant_race"]
|
||
emotion = demography["dominant_emotion"]
|
||
logger.debug(f"Age: {age}")
|
||
logger.debug(f"Gender: {gender}")
|
||
logger.debug(f"Race: {race}")
|
||
logger.debug(f"Emotion: {emotion}")
|
||
assert demography.get("age") is not None
|
||
assert demography.get("dominant_gender") is not None
|
||
assert demography.get("dominant_race") is not None
|
||
assert demography.get("dominant_emotion") is not None
|
||
|
||
logger.info("✅ test analyze for all actions as tuple done")
|
||
|
||
|
||
def test_analyze_with_all_actions_as_list():
|
||
img = "dataset/img4.jpg"
|
||
demography_objs = DeepFace.analyze(
|
||
img, actions=["age", "gender", "race", "emotion"], silent=True
|
||
)
|
||
|
||
for demography in demography_objs:
|
||
logger.debug(f"Demography: {demography}")
|
||
assert type(demography) == dict
|
||
age = demography["age"]
|
||
gender = demography["dominant_gender"]
|
||
race = demography["dominant_race"]
|
||
emotion = demography["dominant_emotion"]
|
||
logger.debug(f"Age: {age}")
|
||
logger.debug(f"Gender: {gender}")
|
||
logger.debug(f"Race: {race}")
|
||
logger.debug(f"Emotion: {emotion}")
|
||
assert demography.get("age") is not None
|
||
assert demography.get("dominant_gender") is not None
|
||
assert demography.get("dominant_race") is not None
|
||
assert demography.get("dominant_emotion") is not None
|
||
|
||
logger.info("✅ test analyze for all actions as array done")
|
||
|
||
|
||
def test_analyze_for_some_actions():
|
||
img = "dataset/img4.jpg"
|
||
demography_objs = DeepFace.analyze(img, ["age", "gender"], silent=True)
|
||
|
||
for demography in demography_objs:
|
||
assert type(demography) == dict
|
||
age = demography["age"]
|
||
gender = demography["dominant_gender"]
|
||
|
||
logger.debug(f"Age: { age }")
|
||
logger.debug(f"Gender: {gender}")
|
||
|
||
assert demography.get("age") is not None
|
||
assert demography.get("dominant_gender") is not None
|
||
|
||
# these are not in actions
|
||
assert demography.get("dominant_race") is None
|
||
assert demography.get("dominant_emotion") is None
|
||
|
||
logger.info("✅ test analyze for some actions done")
|
||
|
||
|
||
def test_analyze_for_preloaded_image():
|
||
img = cv2.imread("dataset/img1.jpg")
|
||
resp_objs = DeepFace.analyze(img, silent=True)
|
||
|
||
# return type should be list of dict for non batch input
|
||
assert isinstance(resp_objs, list)
|
||
|
||
for resp_obj in resp_objs:
|
||
assert isinstance(resp_obj, dict)
|
||
logger.debug(resp_obj)
|
||
assert resp_obj["age"] > 20 and resp_obj["age"] < 40
|
||
assert resp_obj["dominant_gender"] == "Woman"
|
||
|
||
logger.info("✅ test analyze for pre-loaded image done")
|
||
|
||
|
||
def test_analyze_for_different_detectors():
|
||
img_paths = [
|
||
"dataset/img1.jpg",
|
||
"dataset/img5.jpg",
|
||
"dataset/img6.jpg",
|
||
"dataset/img8.jpg",
|
||
"dataset/img1.jpg",
|
||
"dataset/img2.jpg",
|
||
"dataset/img1.jpg",
|
||
"dataset/img2.jpg",
|
||
"dataset/img6.jpg",
|
||
"dataset/img6.jpg",
|
||
]
|
||
|
||
for img_path in img_paths:
|
||
for detector in detectors:
|
||
results = DeepFace.analyze(
|
||
img_path, actions=("gender",), detector_backend=detector, enforce_detection=False
|
||
)
|
||
# return type should be list of dict for non batch input
|
||
assert isinstance(results, list)
|
||
for result in results:
|
||
assert isinstance(result, dict)
|
||
logger.debug(result)
|
||
|
||
# validate keys
|
||
assert "gender" in result.keys()
|
||
assert "dominant_gender" in result.keys() and result["dominant_gender"] in [
|
||
"Man",
|
||
"Woman",
|
||
]
|
||
|
||
# validate probabilities
|
||
if result["dominant_gender"] == "Man":
|
||
assert result["gender"]["Man"] > result["gender"]["Woman"]
|
||
else:
|
||
assert result["gender"]["Man"] < result["gender"]["Woman"]
|
||
|
||
|
||
def test_analyze_for_batched_image_as_list_of_string():
|
||
img_paths = ["dataset/img1.jpg", "dataset/img2.jpg", "dataset/couple.jpg"]
|
||
expected_faces = [1, 1, 2]
|
||
|
||
demography_batch = DeepFace.analyze(img_path=img_paths, silent=True)
|
||
# return type should be list of list of dict for batch input
|
||
assert isinstance(demography_batch, list)
|
||
|
||
# 3 image in batch, so 3 demography objects
|
||
assert len(demography_batch) == len(img_paths)
|
||
|
||
for idx, demography_objs in enumerate(demography_batch):
|
||
assert isinstance(demography_objs, list)
|
||
assert len(demography_objs) == expected_faces[idx]
|
||
for demography_obj in demography_objs:
|
||
assert isinstance(demography_obj, dict)
|
||
|
||
assert demography_obj["age"] > 20 and demography_obj["age"] < 40
|
||
assert demography_obj["dominant_gender"] in ["Woman", "Man"]
|
||
|
||
logger.info("✅ test analyze for batched image as list of string done")
|
||
|
||
|
||
def test_analyze_for_batched_image_as_list_of_numpy():
|
||
img_paths = ["dataset/img1.jpg", "dataset/img2.jpg", "dataset/couple.jpg"]
|
||
expected_faces = [1, 1, 2]
|
||
|
||
imgs = []
|
||
for img_path in img_paths:
|
||
img = cv2.imread(img_path)
|
||
imgs.append(img)
|
||
|
||
demography_batch = DeepFace.analyze(img_path=imgs, silent=True)
|
||
# return type should be list of list of dict for batch input
|
||
assert isinstance(demography_batch, list)
|
||
|
||
# 3 image in batch, so 3 demography objects
|
||
assert len(demography_batch) == len(img_paths)
|
||
|
||
for idx, demography_objs in enumerate(demography_batch):
|
||
assert isinstance(demography_objs, list)
|
||
assert len(demography_objs) == expected_faces[idx]
|
||
for demography_obj in demography_objs:
|
||
assert isinstance(demography_obj, dict)
|
||
|
||
assert demography_obj["age"] > 20 and demography_obj["age"] < 40
|
||
assert demography_obj["dominant_gender"] in ["Woman", "Man"]
|
||
|
||
logger.info("✅ test analyze for batched image as list of numpy done")
|
||
|
||
|
||
def test_analyze_for_numpy_batched_image():
|
||
img1_path = "dataset/img4.jpg"
|
||
img2_path = "dataset/couple.jpg"
|
||
|
||
# Copy and combine the same image to create multiple faces
|
||
img1 = cv2.imread(img1_path)
|
||
img2 = cv2.imread(img2_path)
|
||
|
||
expected_num_faces = [1, 2]
|
||
|
||
img1 = cv2.resize(img1, (500, 500))
|
||
img2 = cv2.resize(img2, (500, 500))
|
||
|
||
img = np.stack([img1, img2])
|
||
assert len(img.shape) == 4 # Check dimension.
|
||
assert img.shape[0] == 2 # Check batch size.
|
||
|
||
demography_batch = DeepFace.analyze(img, silent=True)
|
||
# return type should be list of list of dict for batch input
|
||
|
||
assert isinstance(demography_batch, list)
|
||
|
||
# 2 image in batch, so 2 demography objects.
|
||
assert len(demography_batch) == 2
|
||
|
||
for i, demography_objs in enumerate(demography_batch):
|
||
assert isinstance(demography_objs, list)
|
||
|
||
assert len(demography_objs) == expected_num_faces[i]
|
||
for demography in demography_objs: # Iterate over faces
|
||
assert isinstance(demography, dict)
|
||
|
||
assert demography["age"] > 20 and demography["age"] < 40
|
||
assert demography["dominant_gender"] in ["Woman", "Man"]
|
||
|
||
logger.info("✅ test analyze for multiple faces done")
|
||
|
||
|
||
def test_batch_detect_age_for_multiple_faces():
|
||
# Load test image and resize to model input size
|
||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||
imgs = [img, img]
|
||
results = Age.ApparentAgeClient().predict(imgs)
|
||
# Check there are two ages detected
|
||
assert len(results) == 2
|
||
# Check two faces ages are the same in integer format（e.g. 23.6 -> 23）
|
||
# Must use int() to compare because of max float precision issue in different platforms
|
||
assert np.array_equal(int(results[0]), int(results[1]))
|
||
logger.info("✅ test batch detect age for multiple faces done")
|
||
|
||
|
||
def test_batch_detect_emotion_for_multiple_faces():
|
||
# Load test image and resize to model input size
|
||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||
imgs = [img, img]
|
||
results = Emotion.EmotionClient().predict(imgs)
|
||
# Check there are two emotions detected
|
||
assert len(results) == 2
|
||
# Check two faces emotions are the same
|
||
assert np.array_equal(results[0], results[1])
|
||
logger.info("✅ test batch detect emotion for multiple faces done")
|
||
|
||
|
||
def test_batch_detect_gender_for_multiple_faces():
|
||
# Load test image and resize to model input size
|
||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||
imgs = [img, img]
|
||
results = Gender.GenderClient().predict(imgs)
|
||
# Check there are two genders detected
|
||
assert len(results) == 2
|
||
# Check two genders are the same
|
||
assert np.array_equal(results[0], results[1])
|
||
logger.info("✅ test batch detect gender for multiple faces done")
|
||
|
||
|
||
def test_batch_detect_race_for_multiple_faces():
|
||
# Load test image and resize to model input size
|
||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||
imgs = [img, img]
|
||
results = Race.RaceClient().predict(imgs)
|
||
# Check there are two races detected
|
||
assert len(results) == 2
|
||
# Check two races are the same
|
||
assert np.array_equal(results[0], results[1])
|
||
logger.info("✅ test batch detect race for multiple faces done")
|