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f128fa4754
* check threshold for emotion and race liezs in btw 0 and 100 * Update release.yml * correct order of calling args in test display
289 строки
11 KiB
Python
289 строки
11 KiB
Python
import cv2
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import numpy as np
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import os
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import shutil
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import pathlib
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from tensorflow.keras.models import load_model
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from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
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from tensorflow.keras.preprocessing.image import img_to_array
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from deepface import DeepFace
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from retinaface import RetinaFace
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from ammico.utils import DownloadResource, AnalysisMethod
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DEEPFACE_PATH = ".deepface"
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def deepface_symlink_processor(name):
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def _processor(fname, action, pooch):
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if not os.path.exists(name):
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# symlink does not work on windows
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# use copy if running on windows
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if os.name != "nt":
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os.symlink(fname, name)
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else:
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shutil.copy(fname, name)
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return fname
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return _processor
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face_mask_model = DownloadResource(
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url="https://github.com/chandrikadeb7/Face-Mask-Detection/raw/v1.0.0/mask_detector.model",
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known_hash="sha256:d0b30e2c7f8f187c143d655dee8697fcfbe8678889565670cd7314fb064eadc8",
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)
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deepface_age_model = DownloadResource(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5",
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known_hash="sha256:0aeff75734bfe794113756d2bfd0ac823d51e9422c8961125b570871d3c2b114",
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processor=deepface_symlink_processor(
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pathlib.Path.home().joinpath(DEEPFACE_PATH, "weights", "age_model_weights.h5")
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),
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)
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deepface_face_expression_model = DownloadResource(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5",
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known_hash="sha256:e8e8851d3fa05c001b1c27fd8841dfe08d7f82bb786a53ad8776725b7a1e824c",
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processor=deepface_symlink_processor(
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pathlib.Path.home().joinpath(
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".deepface", "weights", "facial_expression_model_weights.h5"
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)
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),
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)
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deepface_gender_model = DownloadResource(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5",
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known_hash="sha256:45513ce5678549112d25ab85b1926fb65986507d49c674a3d04b2ba70dba2eb5",
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processor=deepface_symlink_processor(
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pathlib.Path.home().joinpath(
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DEEPFACE_PATH, "weights", "gender_model_weights.h5"
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)
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),
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)
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deepface_race_model = DownloadResource(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5",
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known_hash="sha256:eb22b28b1f6dfce65b64040af4e86003a5edccb169a1a338470dde270b6f5e54",
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processor=deepface_symlink_processor(
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pathlib.Path.home().joinpath(
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DEEPFACE_PATH, "weights", "race_model_single_batch.h5"
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)
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),
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)
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retinaface_model = DownloadResource(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5",
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known_hash="sha256:ecb2393a89da3dd3d6796ad86660e298f62a0c8ae7578d92eb6af14e0bb93adf",
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processor=deepface_symlink_processor(
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pathlib.Path.home().joinpath(DEEPFACE_PATH, "weights", "retinaface.h5")
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),
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)
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class EmotionDetector(AnalysisMethod):
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def __init__(
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self,
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subdict: dict,
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emotion_threshold: float = 50.0,
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race_threshold: float = 50.0,
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) -> None:
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"""
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Initializes the EmotionDetector object.
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Args:
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subdict (dict): The dictionary to store the analysis results.
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emotion_threshold (float): The threshold for detecting emotions (default: 50.0).
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race_threshold (float): The threshold for detecting race (default: 50.0).
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"""
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super().__init__(subdict)
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self.subdict.update(self.set_keys())
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# check if thresholds are valid
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if emotion_threshold < 0 or emotion_threshold > 100:
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raise ValueError("Emotion threshold must be between 0 and 100.")
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if race_threshold < 0 or race_threshold > 100:
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raise ValueError("Race threshold must be between 0 and 100.")
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self.emotion_threshold = emotion_threshold
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self.race_threshold = race_threshold
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self.emotion_categories = {
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"angry": "Negative",
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"disgust": "Negative",
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"fear": "Negative",
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"sad": "Negative",
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"happy": "Positive",
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"surprise": "Neutral",
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"neutral": "Neutral",
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}
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def set_keys(self) -> dict:
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"""
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Sets the initial parameters for the analysis.
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Returns:
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dict: The dictionary with initial parameter values.
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"""
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params = {
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"face": "No",
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"multiple_faces": "No",
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"no_faces": 0,
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"wears_mask": ["No"],
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"age": [None],
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"gender": [None],
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"race": [None],
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"emotion": [None],
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"emotion (category)": [None],
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}
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return params
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def analyse_image(self) -> dict:
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"""
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Performs facial expression analysis on the image.
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Returns:
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dict: The updated subdict dictionary with analysis results.
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"""
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return self.facial_expression_analysis()
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def analyze_single_face(self, face: np.ndarray) -> dict:
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"""
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Analyzes the features of a single face.
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Args:
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face (np.ndarray): The face image array.
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Returns:
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dict: The analysis results for the face.
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"""
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fresult = {}
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# Determine whether the face wears a mask
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fresult["wears_mask"] = self.wears_mask(face)
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# Adapt the features we are looking for depending on whether a mask is worn.
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# White masks screw race detection, emotion detection is useless.
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actions = ["age", "gender"]
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if not fresult["wears_mask"]:
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actions = actions + ["race", "emotion"]
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# Ensure that all data has been fetched by pooch
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deepface_age_model.get()
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deepface_face_expression_model.get()
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deepface_gender_model.get()
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deepface_race_model.get()
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# Run the full DeepFace analysis
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fresult.update(
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DeepFace.analyze(
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img_path=face,
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actions=actions,
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prog_bar=False,
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detector_backend="skip",
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)
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)
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# We remove the region, as the data is not correct - after all we are
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# running the analysis on a subimage.
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del fresult["region"]
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return fresult
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def facial_expression_analysis(self) -> dict:
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"""
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Performs facial expression analysis on the image.
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Returns:
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dict: The updated subdict dictionary with analysis results.
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"""
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# Find (multiple) faces in the image and cut them
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retinaface_model.get()
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faces = RetinaFace.extract_faces(self.subdict["filename"])
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# If no faces are found, we return empty keys
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if len(faces) == 0:
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return self.subdict
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# Sort the faces by sight to prioritize prominent faces
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faces = list(reversed(sorted(faces, key=lambda f: f.shape[0] * f.shape[1])))
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self.subdict["face"] = "Yes"
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self.subdict["multiple_faces"] = "Yes" if len(faces) > 1 else "No"
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self.subdict["no_faces"] = len(faces) if len(faces) <= 15 else 99
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# note number of faces being identified
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result = {"number_faces": len(faces) if len(faces) <= 3 else 3}
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# We limit ourselves to three faces
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for i, face in enumerate(faces[:3]):
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result[f"person{ i+1 }"] = self.analyze_single_face(face)
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self.clean_subdict(result)
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return self.subdict
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def clean_subdict(self, result: dict) -> dict:
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"""
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Cleans the subdict dictionary by converting results into appropriate formats.
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Args:
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result (dict): The analysis results.
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Returns:
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dict: The updated subdict dictionary.
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"""
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# Each person subdict converted into list for keys
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self.subdict["wears_mask"] = []
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self.subdict["age"] = []
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self.subdict["gender"] = []
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self.subdict["race"] = []
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self.subdict["emotion"] = []
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self.subdict["emotion (category)"] = []
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for i in range(result["number_faces"]):
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person = "person{}".format(i + 1)
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self.subdict["wears_mask"].append(
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"Yes" if result[person]["wears_mask"] else "No"
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)
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self.subdict["age"].append(result[person]["age"])
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# Gender is now reported as a list of dictionaries.
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# Each dict represents one face.
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# Each dict contains probability for Woman and Man.
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# We take only the higher probability result for each dict.
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self.subdict["gender"].append(result[person]["gender"])
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# Race and emotion are only detected if a person does not wear a mask
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if result[person]["wears_mask"]:
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self.subdict["race"].append(None)
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self.subdict["emotion"].append(None)
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self.subdict["emotion (category)"].append(None)
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elif not result[person]["wears_mask"]:
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# Check whether the race threshold was exceeded
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if (
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result[person]["race"][result[person]["dominant_race"]]
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> self.race_threshold
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):
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self.subdict["race"].append(result[person]["dominant_race"])
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else:
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self.subdict["race"].append(None)
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# Check whether the emotion threshold was exceeded
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if (
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result[person]["emotion"][result[person]["dominant_emotion"]]
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> self.emotion_threshold
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):
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self.subdict["emotion"].append(result[person]["dominant_emotion"])
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self.subdict["emotion (category)"].append(
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self.emotion_categories[result[person]["dominant_emotion"]]
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)
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else:
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self.subdict["emotion"].append(None)
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self.subdict["emotion (category)"].append(None)
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return self.subdict
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def wears_mask(self, face: np.ndarray) -> bool:
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"""
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Determines whether a face wears a mask.
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Args:
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face (np.ndarray): The face image array.
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Returns:
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bool: True if the face wears a mask, False otherwise.
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"""
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global mask_detection_model
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# Preprocess the face to match the assumptions of the face mask detection model
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face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
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face = cv2.resize(face, (224, 224))
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face = img_to_array(face)
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face = preprocess_input(face)
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face = np.expand_dims(face, axis=0)
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# Lazily load the model
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mask_detection_model = load_model(face_mask_model.get())
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# Run the model
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mask, without_mask = mask_detection_model.predict(face)[0]
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# Convert from np.bool_ to bool to later be able to serialize the result
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return bool(mask > without_mask)
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