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