mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-07-26 13:47:03 +02:00
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
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@ -59,7 +59,7 @@ Fine-tune face recognition with these optional parameters:
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### Recognition
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- `model_size`: Which model size to use, options are `small` or `large`
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- `unknown_score`: Min score to mark a person as a potential match, matches below this will be marked as unknown.
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- `unknown_score`: Min score to mark a person as a potential match, matches at or below this will be marked as unknown.
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- Default: `0.8`.
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- `recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
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- Default: `0.9`.
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@ -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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@ -17,6 +17,7 @@
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"createFaceLibrary": {
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"title": "Create Face Library",
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"desc": "Create a new face library",
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"new": "Create New Face",
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"nextSteps": "It is recommended to use the Train tab to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle."
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},
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"train": {
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@ -3,6 +3,7 @@ import TimeAgo from "@/components/dynamic/TimeAgo";
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import AddFaceIcon from "@/components/icons/AddFaceIcon";
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import ActivityIndicator from "@/components/indicators/activity-indicator";
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import CreateFaceWizardDialog from "@/components/overlay/detail/FaceCreateWizardDialog";
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import TextEntryDialog from "@/components/overlay/dialog/TextEntryDialog";
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import UploadImageDialog from "@/components/overlay/dialog/UploadImageDialog";
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import { Button } from "@/components/ui/button";
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import {
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@ -32,13 +33,23 @@ import { useFormattedTimestamp } from "@/hooks/use-date-utils";
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import useKeyboardListener from "@/hooks/use-keyboard-listener";
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import useOptimisticState from "@/hooks/use-optimistic-state";
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import { cn } from "@/lib/utils";
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import { Event } from "@/types/event";
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import { FaceLibraryData, RecognizedFaceData } from "@/types/face";
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import { FaceRecognitionConfig, FrigateConfig } from "@/types/frigateConfig";
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import { TooltipPortal } from "@radix-ui/react-tooltip";
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import axios from "axios";
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import { useCallback, useEffect, useMemo, useRef, useState } from "react";
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import { isDesktop, isMobile } from "react-device-detect";
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import { useTranslation } from "react-i18next";
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import { LuImagePlus, LuRefreshCw, LuScanFace, LuTrash2 } from "react-icons/lu";
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import {
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LuImagePlus,
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LuPlus,
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LuRefreshCw,
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LuScanFace,
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LuSearch,
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LuTrash2,
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} from "react-icons/lu";
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import { useNavigate } from "react-router-dom";
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import { toast } from "sonner";
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import useSWR from "swr";
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@ -391,14 +402,53 @@ function TrainingGrid({
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onClickFace,
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onRefresh,
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}: TrainingGridProps) {
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const { t } = useTranslation(["views/faceLibrary"]);
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const { t } = useTranslation(["views/faceLibrary", "views/explore"]);
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const navigate = useNavigate();
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// face data
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const [selectedEvent, setSelectedEvent] = useState<RecognizedFaceData>();
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const faceGroups = useMemo(() => {
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const groups: { [eventId: string]: RecognizedFaceData[] } = {};
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Array.from(new Set(attemptImages))
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.sort()
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.reverse()
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.forEach((image) => {
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const parts = image.split("-");
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const data = {
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filename: image,
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timestamp: Number.parseFloat(parts[0]),
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eventId: `${parts[0]}-${parts[1]}`,
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name: parts[2],
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score: Number.parseFloat(parts[3]),
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};
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if (groups[data.eventId]) {
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groups[data.eventId].push(data);
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} else {
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groups[data.eventId] = [data];
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}
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});
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return groups;
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}, [attemptImages]);
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const eventIdsQuery = useMemo(
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() => Object.keys(faceGroups).join(","),
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[faceGroups],
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);
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const { data: events } = useSWR<Event[]>([
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"event_ids",
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{ ids: eventIdsQuery },
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]);
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// selection
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const [selectedEvent, setSelectedEvent] = useState<Event>();
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const formattedDate = useFormattedTimestamp(
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selectedEvent?.timestamp ?? 0,
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selectedEvent?.start_time ?? 0,
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config?.ui.time_format == "24hour"
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? t("time.formattedTimestampWithYear.24hour", { ns: "common" })
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: t("time.formattedTimestampWithYear.12hour", { ns: "common" }),
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@ -415,23 +465,32 @@ function TrainingGrid({
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}
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}}
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>
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<DialogContent>
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<DialogContent
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className={cn(
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"",
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selectedEvent?.has_snapshot && isDesktop && "max-w-7xl",
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)}
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>
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<DialogHeader>
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<DialogTitle>{t("details.face")}</DialogTitle>
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<DialogDescription>{t("details.faceDesc")}</DialogDescription>
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</DialogHeader>
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<div className="flex flex-col gap-1.5">
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<div className="text-sm text-primary/40">{t("details.person")}</div>
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<div className="text-sm capitalize">{selectedEvent?.name}</div>
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</div>
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<div className="flex flex-col gap-1.5">
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<div className="text-sm text-primary/40">
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{t("details.confidence")}
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</div>
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<div className="text-sm capitalize">
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{(selectedEvent?.score || 0) * 100}%
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{selectedEvent?.sub_label ?? "Unknown"}
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</div>
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</div>
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{selectedEvent?.data.sub_label_score && (
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<div className="flex flex-col gap-1.5">
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<div className="text-sm text-primary/40">
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{t("details.confidence")}
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</div>
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<div className="text-sm capitalize">
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{Math.round(selectedEvent?.data?.sub_label_score || 0) * 100}%
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</div>
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</div>
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)}
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<div className="flex flex-col gap-1.5">
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<div className="text-sm text-primary/40">
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{t("details.timestamp")}
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@ -440,36 +499,89 @@ function TrainingGrid({
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</div>
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<img
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className="w-full"
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src={`${baseUrl}api/events/${selectedEvent?.eventId}/thumbnail.jpg`}
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src={`${baseUrl}api/events/${selectedEvent?.id}/${selectedEvent?.has_snapshot ? "snapshot.jpg" : "thumbnail.jpg"}`}
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/>
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</DialogContent>
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</Dialog>
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<div className="scrollbar-container flex flex-wrap gap-2 overflow-y-scroll p-1">
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{attemptImages.map((image: string) => (
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<FaceAttempt
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key={image}
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image={image}
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faceNames={faceNames}
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recognitionConfig={config.face_recognition}
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selected={selectedFaces.includes(image)}
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onClick={(data, meta) => {
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if (meta) {
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onClickFace(image, meta);
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} else {
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setSelectedEvent(data);
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}
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}}
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onRefresh={onRefresh}
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/>
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))}
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{Object.entries(faceGroups).map(([key, group]) => {
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const event = events?.find((ev) => ev.id == key);
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return (
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<div
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key={key}
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className={cn(
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"flex flex-col gap-2 rounded-lg bg-card p-2",
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isMobile && "w-full",
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)}
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>
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<div className="flex flex-row justify-between">
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<div className="capitalize">
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Person
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{event?.sub_label
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? `: ${event.sub_label} (${Math.round((event.data.sub_label_score || 0) * 100)}%)`
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: ": Unknown"}
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</div>
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{event && (
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<Tooltip>
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<TooltipTrigger>
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<div
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className="cursor-pointer"
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onClick={() => {
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navigate(`/explore?event_id=${event.id}`);
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}}
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>
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<LuSearch className="size-4 text-muted-foreground" />
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</div>
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</TooltipTrigger>
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<TooltipPortal>
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<TooltipContent>
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{t("details.item.button.viewInExplore", {
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ns: "views/explore",
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})}
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</TooltipContent>
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</TooltipPortal>
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</Tooltip>
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)}
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</div>
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<div
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className={cn(
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"gap-2",
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isDesktop
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? "flex flex-row flex-wrap"
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: "grid grid-cols-2 sm:grid-cols-5 lg:grid-cols-6",
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)}
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>
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{group.map((data: RecognizedFaceData) => (
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<FaceAttempt
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key={data.filename}
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data={data}
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faceNames={faceNames}
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recognitionConfig={config.face_recognition}
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selected={selectedFaces.includes(data.filename)}
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onClick={(data, meta) => {
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if (meta || selectedFaces.length > 0) {
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onClickFace(data.filename, true);
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} else if (event) {
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setSelectedEvent(event);
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}
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}}
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onRefresh={onRefresh}
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/>
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))}
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</div>
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</div>
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);
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})}
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</div>
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</>
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);
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}
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type FaceAttemptProps = {
|
||||
image: string;
|
||||
data: RecognizedFaceData;
|
||||
faceNames: string[];
|
||||
recognitionConfig: FaceRecognitionConfig;
|
||||
selected: boolean;
|
||||
@ -477,7 +589,7 @@ type FaceAttemptProps = {
|
||||
onRefresh: () => void;
|
||||
};
|
||||
function FaceAttempt({
|
||||
image,
|
||||
data,
|
||||
faceNames,
|
||||
recognitionConfig,
|
||||
selected,
|
||||
@ -485,16 +597,6 @@ function FaceAttempt({
|
||||
onRefresh,
|
||||
}: FaceAttemptProps) {
|
||||
const { t } = useTranslation(["views/faceLibrary"]);
|
||||
const data = useMemo<RecognizedFaceData>(() => {
|
||||
const parts = image.split("-");
|
||||
|
||||
return {
|
||||
timestamp: Number.parseFloat(parts[0]),
|
||||
eventId: `${parts[0]}-${parts[1]}`,
|
||||
name: parts[2],
|
||||
score: Number.parseFloat(parts[3]),
|
||||
};
|
||||
}, [image]);
|
||||
|
||||
const scoreStatus = useMemo(() => {
|
||||
if (data.score >= recognitionConfig.recognition_threshold) {
|
||||
@ -508,6 +610,8 @@ function FaceAttempt({
|
||||
|
||||
// interaction
|
||||
|
||||
const [newFace, setNewFace] = useState(false);
|
||||
|
||||
const imgRef = useRef<HTMLImageElement | null>(null);
|
||||
|
||||
useContextMenu(imgRef, () => {
|
||||
@ -519,7 +623,9 @@ function FaceAttempt({
|
||||
const onTrainAttempt = useCallback(
|
||||
(trainName: string) => {
|
||||
axios
|
||||
.post(`/faces/train/${trainName}/classify`, { training_file: image })
|
||||
.post(`/faces/train/${trainName}/classify`, {
|
||||
training_file: data.filename,
|
||||
})
|
||||
.then((resp) => {
|
||||
if (resp.status == 200) {
|
||||
toast.success(t("toast.success.trainedFace"), {
|
||||
@ -538,12 +644,12 @@ function FaceAttempt({
|
||||
});
|
||||
});
|
||||
},
|
||||
[image, onRefresh, t],
|
||||
[data, onRefresh, t],
|
||||
);
|
||||
|
||||
const onReprocess = useCallback(() => {
|
||||
axios
|
||||
.post(`/faces/reprocess`, { training_file: image })
|
||||
.post(`/faces/reprocess`, { training_file: data.filename })
|
||||
.then((resp) => {
|
||||
if (resp.status == 200) {
|
||||
toast.success(t("toast.success.updatedFaceScore"), {
|
||||
@ -561,79 +667,102 @@ function FaceAttempt({
|
||||
position: "top-center",
|
||||
});
|
||||
});
|
||||
}, [image, onRefresh, t]);
|
||||
}, [data, onRefresh, t]);
|
||||
|
||||
return (
|
||||
<div
|
||||
className={cn(
|
||||
"relative flex cursor-pointer flex-col rounded-lg outline outline-[3px]",
|
||||
selected
|
||||
? "shadow-selected outline-selected"
|
||||
: "outline-transparent duration-500",
|
||||
)}
|
||||
>
|
||||
<div className="relative w-full overflow-hidden rounded-t-lg border border-t-0 *:text-card-foreground">
|
||||
<img
|
||||
ref={imgRef}
|
||||
className="size-44"
|
||||
src={`${baseUrl}clips/faces/train/${image}`}
|
||||
onClick={(e) => onClick(data, e.metaKey || e.ctrlKey)}
|
||||
<>
|
||||
{newFace && (
|
||||
<TextEntryDialog
|
||||
open={true}
|
||||
setOpen={setNewFace}
|
||||
title={t("createFaceLibrary.new")}
|
||||
onSave={(newName) => onTrainAttempt(newName)}
|
||||
/>
|
||||
<div className="absolute bottom-1 right-1 z-10 rounded-lg bg-black/50 px-2 py-1 text-xs text-white">
|
||||
<TimeAgo className="text-white" time={data.timestamp * 1000} dense />
|
||||
)}
|
||||
|
||||
<div
|
||||
className={cn(
|
||||
"relative flex cursor-pointer flex-col rounded-lg outline outline-[3px]",
|
||||
selected
|
||||
? "shadow-selected outline-selected"
|
||||
: "outline-transparent duration-500",
|
||||
)}
|
||||
>
|
||||
<div className="relative w-full overflow-hidden rounded-lg *:text-card-foreground">
|
||||
<img
|
||||
ref={imgRef}
|
||||
className={cn("size-44", isMobile && "w-full")}
|
||||
src={`${baseUrl}clips/faces/train/${data.filename}`}
|
||||
onClick={(e) => onClick(data, e.metaKey || e.ctrlKey)}
|
||||
/>
|
||||
<div className="absolute bottom-1 right-1 z-10 rounded-lg bg-black/50 px-2 py-1 text-xs text-white">
|
||||
<TimeAgo
|
||||
className="text-white"
|
||||
time={data.timestamp * 1000}
|
||||
dense
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div className="rounded-b-lg bg-card p-2">
|
||||
<div className="flex w-full flex-row items-center justify-between gap-2">
|
||||
<div className="flex flex-col items-start text-xs text-primary-variant">
|
||||
<div className="capitalize">{data.name}</div>
|
||||
<div
|
||||
className={cn(
|
||||
"",
|
||||
scoreStatus == "match" && "text-success",
|
||||
scoreStatus == "potential" && "text-orange-400",
|
||||
scoreStatus == "unknown" && "text-danger",
|
||||
)}
|
||||
>
|
||||
{Math.round(data.score * 100)}%
|
||||
<div className="p-2">
|
||||
<div className="flex w-full flex-row items-center justify-between gap-2">
|
||||
<div className="flex flex-col items-start text-xs text-primary-variant">
|
||||
<div className="capitalize">{data.name}</div>
|
||||
<div
|
||||
className={cn(
|
||||
"",
|
||||
scoreStatus == "match" && "text-success",
|
||||
scoreStatus == "potential" && "text-orange-400",
|
||||
scoreStatus == "unknown" && "text-danger",
|
||||
)}
|
||||
>
|
||||
{Math.round(data.score * 100)}%
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex flex-row items-start justify-end gap-5 md:gap-4">
|
||||
<Tooltip>
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<TooltipTrigger>
|
||||
<AddFaceIcon className="size-5 cursor-pointer text-primary-variant hover:text-primary" />
|
||||
</TooltipTrigger>
|
||||
</DropdownMenuTrigger>
|
||||
<DropdownMenuContent>
|
||||
<DropdownMenuLabel>{t("trainFaceAs")}</DropdownMenuLabel>
|
||||
<DropdownMenuItem
|
||||
className="flex cursor-pointer gap-2 capitalize"
|
||||
onClick={() => setNewFace(true)}
|
||||
>
|
||||
<LuPlus />
|
||||
{t("createFaceLibrary.new")}
|
||||
</DropdownMenuItem>
|
||||
{faceNames.map((faceName) => (
|
||||
<DropdownMenuItem
|
||||
key={faceName}
|
||||
className="flex cursor-pointer gap-2 capitalize"
|
||||
onClick={() => onTrainAttempt(faceName)}
|
||||
>
|
||||
<LuScanFace />
|
||||
{faceName}
|
||||
</DropdownMenuItem>
|
||||
))}
|
||||
</DropdownMenuContent>
|
||||
</DropdownMenu>
|
||||
<TooltipContent>{t("trainFace")}</TooltipContent>
|
||||
</Tooltip>
|
||||
<Tooltip>
|
||||
<TooltipTrigger>
|
||||
<LuRefreshCw
|
||||
className="size-5 cursor-pointer text-primary-variant hover:text-primary"
|
||||
onClick={() => onReprocess()}
|
||||
/>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent>{t("button.reprocessFace")}</TooltipContent>
|
||||
</Tooltip>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex flex-row items-start justify-end gap-5 md:gap-4">
|
||||
<Tooltip>
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<TooltipTrigger>
|
||||
<AddFaceIcon className="size-5 cursor-pointer text-primary-variant hover:text-primary" />
|
||||
</TooltipTrigger>
|
||||
</DropdownMenuTrigger>
|
||||
<DropdownMenuContent>
|
||||
<DropdownMenuLabel>{t("trainFaceAs")}</DropdownMenuLabel>
|
||||
{faceNames.map((faceName) => (
|
||||
<DropdownMenuItem
|
||||
key={faceName}
|
||||
className="cursor-pointer capitalize"
|
||||
onClick={() => onTrainAttempt(faceName)}
|
||||
>
|
||||
{faceName}
|
||||
</DropdownMenuItem>
|
||||
))}
|
||||
</DropdownMenuContent>
|
||||
</DropdownMenu>
|
||||
<TooltipContent>{t("trainFace")}</TooltipContent>
|
||||
</Tooltip>
|
||||
<Tooltip>
|
||||
<TooltipTrigger>
|
||||
<LuRefreshCw
|
||||
className="size-5 cursor-pointer text-primary-variant hover:text-primary"
|
||||
onClick={() => onReprocess()}
|
||||
/>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent>{t("button.reprocessFace")}</TooltipContent>
|
||||
</Tooltip>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
||||
@ -643,6 +772,8 @@ type FaceGridProps = {
|
||||
onDelete: (name: string, ids: string[]) => void;
|
||||
};
|
||||
function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
|
||||
const sortedFaces = useMemo(() => faceImages.sort().reverse(), [faceImages]);
|
||||
|
||||
return (
|
||||
<div
|
||||
className={cn(
|
||||
@ -650,7 +781,7 @@ function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
|
||||
isDesktop ? "flex flex-wrap" : "grid grid-cols-2",
|
||||
)}
|
||||
>
|
||||
{faceImages.map((image: string) => (
|
||||
{sortedFaces.map((image: string) => (
|
||||
<FaceImage
|
||||
key={image}
|
||||
name={pageToggle}
|
||||
|
@ -3,6 +3,7 @@ export type FaceLibraryData = {
|
||||
};
|
||||
|
||||
export type RecognizedFaceData = {
|
||||
filename: string;
|
||||
timestamp: number;
|
||||
eventId: string;
|
||||
name: string;
|
||||
|
Loading…
Reference in New Issue
Block a user