mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-08-07 03:11:15 +03:00
Refactor face library page (#17424)
* Section faces by event id * Make score keeping more robust * layout improvements * Cleanup dialog * Fix clicking behavior * Add view in explore option * math.round * Don't require events * Cleanup * Remove selection * Don't require * Change dialog size with snapshot * Use filename as key * fix key * Rework layout for mobile * Handle mobile landscape * Fix train issue * Match logic * Move deletion logic * Fix reprocessing * Support creating a new face * Translations * Do sorting in frontend * Adjust unknown * Cleanup * Set max limit to faces to recognize * Fix sorting * Fix
This commit is contained in:
Nicolas Mowen
@@ -41,13 +41,9 @@ def get_faces():
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face_dict[name] = []
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for file in sorted(
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filter(
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lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
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os.listdir(face_dir),
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),
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key=lambda f: os.path.getctime(os.path.join(face_dir, f)),
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reverse=True,
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for file in filter(
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lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
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os.listdir(face_dir),
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):
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face_dict[name].append(file)
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@@ -125,10 +121,13 @@ def train_face(request: Request, name: str, body: dict = None):
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sanitized_name = sanitize_filename(name)
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rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
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new_name = f"{sanitized_name}-{rand_id}.webp"
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new_file = os.path.join(FACE_DIR, f"{sanitized_name}/{new_name}")
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new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
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if not os.path.exists(new_file_folder):
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os.mkdir(new_file_folder)
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if training_file_name:
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shutil.move(training_file, new_file)
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shutil.move(training_file, os.path.join(new_file_folder, new_name))
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else:
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try:
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event: Event = Event.get(Event.id == event_id)
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@@ -155,7 +154,7 @@ def train_face(request: Request, name: str, body: dict = None):
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x2 = x1 + int(face_box[2] * detect_config.width) - 4
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y2 = y1 + int(face_box[3] * detect_config.height) - 4
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face = snapshot[y1:y2, x1:x2]
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cv2.imwrite(new_file, face)
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cv2.imwrite(os.path.join(new_file_folder, new_name), face)
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context: EmbeddingsContext = request.app.embeddings
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context.clear_face_classifier()
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@@ -33,7 +33,8 @@ logger = logging.getLogger(__name__)
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MAX_DETECTION_HEIGHT = 1080
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MIN_MATCHING_FACES = 2
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MAX_FACES_ATTEMPTS_AFTER_REC = 6
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MAX_FACE_ATTEMPTS = 12
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class FaceRealTimeProcessor(RealTimeProcessorApi):
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@@ -170,6 +171,23 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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)
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return
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# check if we have hit limits
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if (
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id in self.person_face_history
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and len(self.person_face_history[id]) >= MAX_FACES_ATTEMPTS_AFTER_REC
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):
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# if we are at max attempts after rec and we have a rec
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if obj_data.get("sub_label"):
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logger.debug(
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"Not processing due to hitting max attempts after true recognition."
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)
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return
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# if we don't have a rec and are at max attempts
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if len(self.person_face_history[id]) >= MAX_FACE_ATTEMPTS:
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logger.debug("Not processing due to hitting max rec attempts.")
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return
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face: Optional[dict[str, any]] = None
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if self.requires_face_detection:
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@@ -241,7 +259,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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sub_label, score = res
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if score < self.face_config.unknown_score:
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if score <= self.face_config.unknown_score:
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sub_label = "unknown"
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logger.debug(
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@@ -255,13 +273,23 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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os.makedirs(folder, exist_ok=True)
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cv2.imwrite(file, face_frame)
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files = sorted(
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filter(lambda f: (f.endswith(".webp")), os.listdir(folder)),
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key=lambda f: os.path.getctime(os.path.join(folder, f)),
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reverse=True,
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)
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# delete oldest face image if maximum is reached
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if len(files) > self.config.face_recognition.save_attempts:
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os.unlink(os.path.join(folder, files[-1]))
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if id not in self.person_face_history:
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self.person_face_history[id] = []
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self.person_face_history[id].append(
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(sub_label, score, face_frame.shape[0] * face_frame.shape[1])
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)
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(weighted_sub_label, weighted_score) = self.weighted_average_by_area(
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(weighted_sub_label, weighted_score) = self.weighted_average(
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self.person_face_history[id]
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)
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@@ -297,6 +325,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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sub_label, score = res
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if score <= self.face_config.unknown_score:
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sub_label = "unknown"
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return {"success": True, "score": score, "face_name": sub_label}
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elif topic == EmbeddingsRequestEnum.register_face.value:
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rand_id = "".join(
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@@ -366,6 +397,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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sub_label, score = res
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if score <= self.face_config.unknown_score:
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sub_label = "unknown"
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if self.config.face_recognition.save_attempts:
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# write face to library
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folder = os.path.join(FACE_DIR, "train")
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@@ -375,38 +409,49 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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)
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shutil.move(current_file, new_file)
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files = sorted(
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filter(lambda f: (f.endswith(".webp")), os.listdir(folder)),
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key=lambda f: os.path.getctime(os.path.join(folder, f)),
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reverse=True,
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)
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# delete oldest face image if maximum is reached
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if len(files) > self.config.face_recognition.save_attempts:
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os.unlink(os.path.join(folder, files[-1]))
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def expire_object(self, object_id: str):
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if object_id in self.person_face_history:
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self.person_face_history.pop(object_id)
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def weighted_average_by_area(self, results_list: list[tuple[str, float, int]]):
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score_count = {}
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def weighted_average(
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self, results_list: list[tuple[str, float, int]], max_weight: int = 4000
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):
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"""
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Calculates a robust weighted average, capping the area weight and giving more weight to higher scores.
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Args:
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results_list: A list of tuples, where each tuple contains (name, score, face_area).
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max_weight: The maximum weight to apply based on face area.
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Returns:
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A tuple containing the prominent name and its weighted average score, or (None, 0.0) if the list is empty.
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"""
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if not results_list:
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return None, 0.0
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weighted_scores = {}
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total_face_areas = {}
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total_weights = {}
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for name, score, face_area in results_list:
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if name == "unknown":
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continue
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if name not in weighted_scores:
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score_count[name] = 1
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weighted_scores[name] = 0.0
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total_face_areas[name] = 0.0
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else:
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score_count[name] += 1
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total_weights[name] = 0.0
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weighted_scores[name] += score * face_area
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total_face_areas[name] += face_area
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# Capped weight based on face area
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weight = min(face_area, max_weight)
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prominent_name = max(score_count)
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# Score-based weighting (higher scores get more weight)
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weight *= (score - self.face_config.unknown_score) * 10
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weighted_scores[name] += score * weight
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total_weights[name] += weight
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return prominent_name, weighted_scores[prominent_name] / total_face_areas[
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prominent_name
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]
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if not weighted_scores:
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return None, 0.0
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best_name = max(weighted_scores, key=weighted_scores.get)
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weighted_average = weighted_scores[best_name] / total_weights[best_name]
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return best_name, weighted_average
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