Add back duration modification

docs/docs/configuration/authentication.md

@@ -97,7 +97,7 @@ python3 -c 'import secrets; print(secrets.token_hex(64))'
 
 ### Header mapping
 
-If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Role` values. Header names are not case sensitive.
+If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Role` values. Header names are not case sensitive. Multiple values can be included in the role header, but they must be comma-separated.
 
 ```yaml
 proxy:

docs/docs/configuration/face_recognition.md

@@ -105,19 +105,21 @@ When choosing images to include in the face training set it is recommended to al
 
 :::
 
+### Understanding the Train Tab
+
+The Train tab on the face library is used to see recent face recognition attempts. The face images are grouped by the person object that they were detected as being related to. Under each face image there will be a name (or `Unknown`) and the confidence of that recognition. Each image can be used to train as a specific person, but not every image should be trained. See below for guidance on training.
+
 ### Step 1 - Building a Strong Foundation
 
 When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 photos containing just this person's face. It is important that the person's face in the photo is front-facing and not turned, this will ensure a good starting point.
 
-Then it is recommended to use the `Face Library` tab in Frigate 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 front-facing. Ignore images from cameras that recognize faces from an angle.
+Then it is recommended to use the `Face Library` tab in Frigate 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 front-facing. Ignore images from cameras that recognize faces from an angle. Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have over-fitting.
 
-Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have over-fitting.
-
-Once a person starts to be consistently recognized correctly on images that are front-facing, it is time to move on to the next step.
+You do not want to train images that are 90%+ as these are already being confidently recognized. In this step the goal is to train on clear, lower scoring front-facing images until the majority of front-facing images for a given person are consistently recognized correctly. Then it is time to move on to step 2.
 
 ### Step 2 - Expanding The Dataset
 
-Once front-facing images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.
+Once front-facing images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone, and you still only want to train on images that score lower. 
 
 ## FAQ
 

docs/docs/configuration/zones.md

@@ -165,7 +165,7 @@ These speed values are output as a number in miles per hour (mph) or kilometers
 
 #### Best practices and caveats
 
-- Speed estimation works best with a straight road or path when your object travels in a straight line across that path. Avoid creating your zone near intersections or anywhere that objects would make a turn. If the bounding box changes shape (either because the object made a turn or became partially obscured, for example), speed estimation will not be accurate.
+- Speed estimation works best with a straight road or path when your object travels in a straight line across that path. Avoid creating your zone near intersections or anywhere that objects would make a turn. A large zone can be used, but it's not required and may even cause issues - if the object's bounding box changes shape (such as when it turns or becomes partially hidden), the speed estimate will be inaccurate. Generally it's best to make your zone large enough to capture a few frames, but small enough so that the bounding box doesn't change size as it travels through the zone.
 - Create a zone where the bottom center of your object's bounding box travels directly through it and does not become obscured at any time. See the photo example above.
 - Depending on the size and location of your zone, you may want to decrease the zone's `inertia` value from the default of 3.
 - The more accurate your real-world dimensions can be measured, the more accurate speed estimation will be. However, due to the way Frigate's tracking algorithm works, you may need to tweak the real-world distance values so that estimated speeds better match real-world speeds.

frigate/api/media.py

@@ -551,7 +551,6 @@ def recording_clip(
     camera_name: str,
     start_ts: float,
     end_ts: float,
-    trim: str = "start,end",
 ):
     def run_download(ffmpeg_cmd: list[str], file_path: str):
         with sp.Popen(
@@ -595,13 +594,12 @@ def recording_clip(
         for clip in recordings:
             file.write(f"file '{clip.path}'\n")
 
-            # if this is the starting clip and start trim is enabled, add an inpoint
-            if "start" in trim and clip.start_time < start_ts:
+            # if this is the starting clip, add an inpoint
+            if clip.start_time < start_ts:
                 file.write(f"inpoint {int(start_ts - clip.start_time)}\n")
 
             # if this is the ending clip and end trim is enabled, add an outpoint
-            # we don't trim the output because the VOD also removes outpoint
-            if "end" in trim and clip.end_time > end_ts:
+            if clip.end_time > end_ts:
                 file.write(f"outpoint {int(end_ts - clip.start_time)}\n")
 
     if len(file_name) > 1000:
@@ -645,7 +643,12 @@ def recording_clip(
 @router.get("/vod/{camera_name}/start/{start_ts}/end/{end_ts}")
 def vod_ts(camera_name: str, start_ts: float, end_ts: float):
     recordings = (
-        Recordings.select(Recordings.path, Recordings.duration, Recordings.end_time)
+        Recordings.select(
+            Recordings.path,
+            Recordings.duration,
+            Recordings.end_time,
+            Recordings.start_time,
+        )
         .where(
             Recordings.start_time.between(start_ts, end_ts)
             | Recordings.end_time.between(start_ts, end_ts)
@@ -665,14 +668,19 @@ def vod_ts(camera_name: str, start_ts: float, end_ts: float):
         clip = {"type": "source", "path": recording.path}
         duration = int(recording.duration * 1000)
 
-        # Determine if we need to end the last clip early
+        # adjust start offset if start_ts is after recording.start_time
+        if start_ts > recording.start_time:
+            inpoint = int((start_ts - recording.start_time) * 1000)
+            clip["clipFrom"] = inpoint
+            duration -= inpoint
+
+        # adjust end if recording.end_time is after end_ts
         if recording.end_time > end_ts:
             duration -= int((recording.end_time - end_ts) * 1000)
 
-            if duration == 0:
-                # this means the segment starts right at the end of the requested time range
-                # and it does not need to be included
-                continue
+        if duration <= 0:
+            # skip if the clip has no valid duration
+            continue
 
         if 0 < duration < max_duration_ms:
             clip["keyFrameDurations"] = [duration]

frigate/camera/state.py

@@ -282,9 +282,13 @@ class CameraState:
             }
             new_obj.thumbnail_data = thumbnail_data
             tracked_objects[id].thumbnail_data = thumbnail_data
-            self.best_objects[new_obj.obj_data["label"]] = new_obj
+            object_type = new_obj.obj_data["label"]
+            self.best_objects[object_type] = new_obj
 
             # call event handlers
+            for c in self.callbacks["snapshot"]:
+                c(self.name, self.best_objects[object_type], frame_name)
+
             for c in self.callbacks["start"]:
                 c(self.name, new_obj, frame_name)
 

frigate/config/classification.py

@@ -78,7 +78,7 @@ class FaceRecognitionConfig(FrigateBaseModel):
         le=1.0,
     )
     min_area: int = Field(
-        default=500, title="Min area of face box to consider running face recognition."
+        default=750, title="Min area of face box to consider running face recognition."
     )
     save_attempts: int = Field(
         default=100, ge=0, title="Number of face attempts to save in the train tab."
@@ -91,7 +91,7 @@ class FaceRecognitionConfig(FrigateBaseModel):
 class CameraFaceRecognitionConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable face recognition.")
     min_area: int = Field(
-        default=500, title="Min area of face box to consider running face recognition."
+        default=750, title="Min area of face box to consider running face recognition."
     )
 
     model_config = ConfigDict(extra="forbid", protected_namespaces=())

frigate/ptz/autotrack.py

@@ -1171,7 +1171,20 @@ class PtzAutoTracker:
             zoom_predicted_movement_time = 0
 
             if np.any(average_velocity):
-                zoom_predicted_movement_time = abs(zoom) * self.zoom_time[camera]
+                # Calculate the intended change in zoom level
+                zoom_change = (1 - abs(zoom)) * (1 if zoom >= 0 else -1)
+
+                # Calculate new zoom level and clamp to [0, 1]
+                new_zoom = max(
+                    0, min(1, self.ptz_metrics[camera].zoom_level.value + zoom_change)
+                )
+
+                # Calculate the actual zoom distance
+                zoom_distance = abs(
+                    new_zoom - self.ptz_metrics[camera].zoom_level.value
+                )
+
+                zoom_predicted_movement_time = zoom_distance * self.zoom_time[camera]
 
                 zoom_predicted_box = (
                     predicted_box
@@ -1188,7 +1201,7 @@ class PtzAutoTracker:
                 tilt = (0.5 - (centroid_y / camera_height)) * 2
 
             logger.debug(
-                f"{camera}: Zoom amount: {zoom}, zoom predicted time: {zoom_predicted_movement_time}, zoom predicted box: {tuple(zoom_predicted_box)}"
+                f"{camera}: Zoom amount: {zoom}, zoom distance: {zoom_distance}, zoom predicted time: {zoom_predicted_movement_time}, zoom predicted box: {tuple(zoom_predicted_box)}"
             )
 
         self._enqueue_move(camera, obj.obj_data["frame_time"], pan, tilt, zoom)

web/public/locales/en/components/filter.json

@@ -17,6 +17,7 @@
     }
   },
   "dates": {
+    "selectPreset": "Select a Preset…",
     "all": {
       "title": "All Dates",
       "short": "Dates"

web/public/locales/en/views/explore.json

@@ -43,6 +43,8 @@
     "adjustAnnotationSettings": "Adjust annotation settings",
     "scrollViewTips": "Scroll to view the significant moments of this object's lifecycle.",
     "autoTrackingTips": "Bounding box positions will be inaccurate for autotracking cameras.",
+    "count": "{{first}} of {{second}}",
+    "trackedPoint": "Tracked Point",
     "lifecycleItemDesc": {
       "visible": "{{label}} detected",
       "entered_zone": "{{label}} entered {{zones}}",

web/src/components/overlay/detail/ObjectLifecycle.tsx

@@ -525,7 +525,10 @@ export default function ObjectLifecycle({
           {t("objectLifecycle.scrollViewTips")}
         </div>
         <div className="min-w-20 text-right text-sm text-muted-foreground">
-          {current + 1} of {eventSequence.length}
+          {t("objectLifecycle.count", {
+            first: current + 1,
+            second: eventSequence.length,
+          })}
         </div>
       </div>
       {config?.cameras[event.camera]?.onvif.autotracking.enabled_in_config && (

web/src/components/overlay/detail/ObjectPath.tsx

@@ -7,6 +7,7 @@ import {
 } from "@/components/ui/tooltip";
 import { TooltipPortal } from "@radix-ui/react-tooltip";
 import { getLifecycleItemDescription } from "@/utils/lifecycleUtil";
+import { useTranslation } from "react-i18next";
 
 type ObjectPathProps = {
   positions?: Position[];
@@ -40,6 +41,7 @@ export function ObjectPath({
   onPointClick,
   visible = true,
 }: ObjectPathProps) {
+  const { t } = useTranslation(["views/explore"]);
   const getAbsolutePositions = useCallback(() => {
     if (!imgRef.current || !positions) return [];
     const imgRect = imgRef.current.getBoundingClientRect();
@@ -103,7 +105,7 @@ export function ObjectPath({
             <TooltipContent side="top" className="smart-capitalize">
               {pos.lifecycle_item
                 ? getLifecycleItemDescription(pos.lifecycle_item)
-                : "Tracked point"}
+                : t("objectLifecycle.trackedPoint")}
             </TooltipContent>
           </TooltipPortal>
         </Tooltip>

web/src/components/overlay/detail/SearchDetailDialog.tsx

@@ -864,16 +864,14 @@ function ObjectDetailsTab({
             className={cn("flex w-full flex-row gap-2", isMobile && "flex-col")}
           >
             {config?.semantic_search.enabled &&
+              setSimilarity != undefined &&
               search.data.type == "object" && (
                 <Button
                   className="w-full"
                   aria-label={t("itemMenu.findSimilar.aria")}
                   onClick={() => {
                     setSearch(undefined);
-
-                    if (setSimilarity) {
-                      setSimilarity();
-                    }
+                    setSimilarity();
                   }}
                 >
                   <div className="flex gap-1">
@@ -1101,7 +1099,7 @@ export function ObjectSnapshotTab({
                     <Tooltip>
                       <TooltipTrigger asChild>
                         <a
-                          href={`${baseUrl}api/events/${search?.id}/snapshot.jpg`}
+                          href={`${baseUrl}api/events/${search?.id}/snapshot.jpg?bbox=1`}
                           download={`${search?.camera}_${search?.label}.jpg`}
                         >
                           <Chip className="cursor-pointer rounded-md bg-gray-500 bg-gradient-to-br from-gray-400 to-gray-500">

web/src/components/overlay/dialog/PlatformAwareDialog.tsx

@@ -91,7 +91,7 @@ export function PlatformAwareSheet({
               className="mx-2"
               onClose={() => onOpenChange(false)}
             >
-              <MobilePageTitle>More Filters</MobilePageTitle>
+              <MobilePageTitle>{title}</MobilePageTitle>
             </MobilePageHeader>
             <div className={contentClassName}>{content}</div>
           </MobilePageContent>

web/src/components/overlay/dialog/SearchFilterDialog.tsx

@@ -224,6 +224,7 @@ export default function SearchFilterDialog({
   return (
     <PlatformAwareSheet
       trigger={trigger}
+      title={t("more")}
       content={content}
       contentClassName={cn(
         "w-auto lg:min-w-[275px] scrollbar-container h-full overflow-auto px-4",

web/src/components/ui/calendar-range.tsx

@@ -369,7 +369,11 @@ export function DateRangePicker({
                 }}
               >
                 <SelectTrigger className="mx-auto mb-2 w-[180px]">
-                  <SelectValue placeholder="Select..." />
+                  <SelectValue
+                    placeholder={t("dates.selectPreset", {
+                      ns: "components/filter",
+                    })}
+                  />
                 </SelectTrigger>
                 <SelectContent>
                   {PRESETS.map((preset) => (

web/src/pages/FaceLibrary.tsx

@@ -31,11 +31,6 @@ import {
   DropdownMenuTrigger,
   DropdownMenuSeparator,
 } from "@/components/ui/dropdown-menu";
-import {
-  Popover,
-  PopoverContent,
-  PopoverTrigger,
-} from "@/components/ui/popover";
 import { Toaster } from "@/components/ui/sonner";
 import {
   Tooltip,
@@ -43,7 +38,6 @@ import {
   TooltipTrigger,
 } from "@/components/ui/tooltip";
 import useContextMenu from "@/hooks/use-contextmenu";
-import { useFormattedTimestamp } from "@/hooks/use-date-utils";
 import useKeyboardListener from "@/hooks/use-keyboard-listener";
 import useOptimisticState from "@/hooks/use-optimistic-state";
 import { cn } from "@/lib/utils";
@@ -58,7 +52,6 @@ import { Trans, useTranslation } from "react-i18next";
 import {
   LuFolderCheck,
   LuImagePlus,
-  LuInfo,
   LuPencil,
   LuRefreshCw,
   LuScanFace,
@@ -68,6 +61,10 @@ import {
 import { useNavigate } from "react-router-dom";
 import { toast } from "sonner";
 import useSWR from "swr";
+import SearchDetailDialog, {
+  SearchTab,
+} from "@/components/overlay/detail/SearchDetailDialog";
+import { SearchResult } from "@/types/search";
 
 export default function FaceLibrary() {
   const { t } = useTranslation(["views/faceLibrary"]);
@@ -663,18 +660,7 @@ function TrainingGrid({
   // selection
 
   const [selectedEvent, setSelectedEvent] = useState<Event>();
-
-  const formattedDate = useFormattedTimestamp(
-    selectedEvent?.start_time ?? 0,
-    config?.ui.time_format == "24hour"
-      ? t("time.formattedTimestampMonthDayYearHourMinute.24hour", {
-          ns: "common",
-        })
-      : t("time.formattedTimestampMonthDayYearHourMinute.12hour", {
-          ns: "common",
-        }),
-    config?.ui.timezone,
-  );
+  const [dialogTab, setDialogTab] = useState<SearchTab>("details");
 
   if (attemptImages.length == 0) {
     return (
@@ -687,66 +673,16 @@ function TrainingGrid({
 
   return (
     <>
-      <Dialog
-        open={selectedEvent != undefined}
-        onOpenChange={(open) => {
-          if (!open) {
-            setSelectedEvent(undefined);
-          }
-        }}
-      >
-        <DialogContent
-          className={cn(
-            "",
-            selectedEvent?.has_snapshot && isDesktop && "max-w-7xl",
-          )}
-        >
-          <DialogHeader>
-            <DialogTitle>{t("details.face")}</DialogTitle>
-            <DialogDescription>{t("details.faceDesc")}</DialogDescription>
-          </DialogHeader>
-          <div className="flex flex-col gap-1.5">
-            <div className="text-sm text-primary/40">{t("details.person")}</div>
-            <div className="text-sm smart-capitalize">
-              {selectedEvent?.sub_label ?? t("details.unknown")}
-            </div>
-          </div>
-          {selectedEvent?.data.sub_label_score && (
-            <div className="flex flex-col gap-1.5">
-              <div className="text-sm text-primary/40">
-                <div className="flex flex-row items-center gap-1">
-                  {t("details.subLabelScore")}
-                  <Popover>
-                    <PopoverTrigger asChild>
-                      <div className="cursor-pointer p-0">
-                        <LuInfo className="size-4" />
-                        <span className="sr-only">Info</span>
-                      </div>
-                    </PopoverTrigger>
-                    <PopoverContent className="w-80">
-                      {t("details.scoreInfo")}
-                    </PopoverContent>
-                  </Popover>
-                </div>
-              </div>
-              <div className="text-sm smart-capitalize">
-                {Math.round((selectedEvent?.data?.sub_label_score || 0) * 100)}%
-              </div>
-            </div>
-          )}
-          <div className="flex flex-col gap-1.5">
-            <div className="text-sm text-primary/40">
-              {t("details.timestamp")}
-            </div>
-            <div className="text-sm">{formattedDate}</div>
-          </div>
-          <img
-            className="mx-auto max-h-[60dvh] object-contain"
-            loading="lazy"
-            src={`${baseUrl}api/events/${selectedEvent?.id}/${selectedEvent?.has_snapshot ? "snapshot.jpg" : "thumbnail.jpg"}`}
-          />
-        </DialogContent>
-      </Dialog>
+      <SearchDetailDialog
+        search={
+          selectedEvent ? (selectedEvent as unknown as SearchResult) : undefined
+        }
+        page={dialogTab}
+        setSimilarity={undefined}
+        setSearchPage={setDialogTab}
+        setSearch={(search) => setSelectedEvent(search as unknown as Event)}
+        setInputFocused={() => {}}
+      />
 
       <div className="scrollbar-container flex flex-wrap gap-2 overflow-y-scroll p-1">
         {Object.entries(faceGroups).map(([key, group]) => {
@@ -853,11 +789,18 @@ function FaceAttemptGroup({
       }}
     >
       <div className="flex flex-row justify-between">
-        <div className="select-none smart-capitalize">
-          Person
-          {event?.sub_label
-            ? `: ${event.sub_label} (${Math.round((event.data.sub_label_score || 0) * 100)}%)`
-            : ": " + t("details.unknown")}
+        <div className="flex flex-col gap-1">
+          <div className="select-none smart-capitalize">
+            Person
+            {event?.sub_label
+              ? `: ${event.sub_label} (${Math.round((event.data.sub_label_score || 0) * 100)}%)`
+              : ": " + t("details.unknown")}
+          </div>
+          <TimeAgo
+            className="text-sm text-secondary-foreground"
+            time={group[0].timestamp * 1000}
+            dense
+          />
         </div>
         {event && (
           <Tooltip>
@@ -950,6 +893,14 @@ function FaceAttempt({
     onClick(data, true);
   });
 
+  const imageArea = useMemo(() => {
+    if (!imgRef.current) {
+      return undefined;
+    }
+
+    return imgRef.current.naturalWidth * imgRef.current.naturalHeight;
+  }, [imgRef]);
+
   // api calls
 
   const onTrainAttempt = useCallback(
@@ -1021,13 +972,11 @@ function FaceAttempt({
               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>
+          {imageArea != undefined && (
+            <div className="absolute bottom-1 right-1 z-10 rounded-lg bg-black/50 px-2 py-1 text-xs text-white">
+              {imageArea}px
+            </div>
+          )}
         </div>
         <div className="select-none p-2">
           <div className="flex w-full flex-row items-center justify-between gap-2">

web/src/views/live/LiveCameraView.tsx

@@ -631,6 +631,7 @@ export default function LiveCameraView({
         <div className="flex flex-col items-center justify-center">
           <PtzControlPanel
             camera={camera.name}
+            enabled={cameraEnabled}
             clickOverlay={clickOverlay}
             setClickOverlay={setClickOverlay}
           />
@@ -689,15 +690,19 @@ function TooltipButton({
 
 function PtzControlPanel({
   camera,
+  enabled,
   clickOverlay,
   setClickOverlay,
 }: {
   camera: string;
+  enabled: boolean;
   clickOverlay: boolean;
   setClickOverlay: React.Dispatch<React.SetStateAction<boolean>>;
 }) {
   const { t } = useTranslation(["views/live"]);
-  const { data: ptz } = useSWR<CameraPtzInfo>(`${camera}/ptz/info`);
+  const { data: ptz } = useSWR<CameraPtzInfo>(
+    enabled ? `${camera}/ptz/info` : null,
+  );
 
   const { send: sendPtz } = usePtzCommand(camera);