Store Intelligence — Visual Analysis

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The camera does NOT move. People cross a door threshold visible in the frame, either entering the store (moving inward / away from camera) or exiting (moving outward / toward camera).\\n\\nA SINGLE PERSON CAN ENTER AND EXIT THE STORE MULTIPLE TIMES during the video. You must track EVERY entry and EVERY exit as a separate event, in chronological order, in the `events` array.\\n\\nYour task: produce structured per-person analytics + per-second positional tracking. Accuracy matters more than exhaustiveness \\u2014 omit an uncertain detection rather than fabricate one.\\n\\n\\u2550\\u2550\\u2550 VISIBILITY RULES \\u2550\\u2550\\u2550\\n\\n1. PERSONS LIST: Only include a person if their head and torso are CLEARLY visible at some point in the video \\u2014 clear enough that a human reviewer could pick them out of a lineup from your description.\\n\\n2. FRAMES ARRAY: Only include a person at timestamp `t` if their TORSO CENTER is clearly inside the visible frame at that exact moment \\u2014 meaning a real human looking at that single frame would say \\\"yes, that person is in the picture right now.\\\" Do NOT include people who are:\\n - Mostly off-screen (about to enter or just left)\\n - Behind another person and not directly visible\\n - Too small/distant to locate confidently\\n - Only inferred from prior or future frames (\\\"about to appear\\\" / \\\"was just there\\\")\\n\\n\\u2550\\u2550\\u2550 COORDINATE SYSTEM \\u2550\\u2550\\u2550\\n\\nNormalized 0\\u20131000 coordinates:\\n- [x=0, y=0] = TOP-LEFT corner\\n- [x=1000, y=1000] = BOTTOM-RIGHT corner\\n- [x=500, y=500] = exact center\\n- A person at the right edge has x \\u2248 900\\u20131000\\n\\nUse the position of the person's TORSO CENTER (not head) so markers sit stably on their body.\\n\\nCALIBRATION CHECK (mandatory): In P1's `notes` field, include one sentence stating where the door is located in the frame in these coordinates. Example: \\\"Calibration: door spans approximately x=300 to x=600, y=200 to y=700.\\\"\\n\\n\\u2550\\u2550\\u2550 CONFIDENCE CALIBRATION \\u2550\\u2550\\u2550\\n\\nDo NOT default to 0.9 for everything:\\n- 0.85\\u20131.0 = certain (clear face across multiple frames)\\n- 0.55\\u20130.84 = probable (partial face OR strong body language)\\n- 0.30\\u20130.54 = guess (clothing/build only, face obscured)\\n- below 0.5 = set the value to \\\"unknown\\\" instead of guessing\\n\\nFor face-masked subjects: gender/age confidence should typically be 0.4\\u20130.6, not 0.9.\\n\\n\\u2550\\u2550\\u2550 IDENTITY \\u2550\\u2550\\u2550\\n\\n- Same physical person across the video = same person_id, always. If they leave and come back wearing different clothes, it is STILL the same person_id (note the change in `notes`).\\n- Use stable visual features: clothing color/pattern, headwear, accessories, carried items, build, hair color, beard, glasses.\\n- If two appearances might be the same person but you cannot confirm, create separate IDs and explain ambiguity in `notes` of both.\\n\\n\\u2550\\u2550\\u2550 TIMING & EVENTS \\u2550\\u2550\\u2550\\n\\n- first_seen_seconds = first moment the person is CLEARLY identifiable in the video. Be generous \\u2014 if they are visible enough to assign a position, mark them as first seen. Do NOT artificially delay this; report when they actually appear, even if that is t=0.\\n- last_seen_seconds = last moment clearly visible in the video.\\n\\n- events = ordered list of EVERY door crossing for this person, in chronological order:\\n [\\n { \\\"t\\\": 22, \\\"type\\\": \\\"entry\\\" },\\n { \\\"t\\\": 28, \\\"type\\\": \\\"exit\\\" },\\n { \\\"t\\\": 35, \\\"type\\\": \\\"entry\\\" },\\n { \\\"t\\\": 41, \\\"type\\\": \\\"exit\\\" }\\n ]\\n Record EVERY entry AND EVERY exit. A person who walks in, walks out, and walks back in produces THREE events. A person who walks in and out three times produces SIX events. There is no upper limit. If the person never crosses the door (just walks past), `events` is an empty array `[]`.\\n\\n- total_dwell_seconds = total cumulative time this person was INSIDE the store during the video. Compute by walking the events in order: the time between each entry and its following exit counts as dwell. If the person is still inside at video end (events list ends with an entry), count up to `last_seen_seconds`. If you cannot determine, set to null.\\n\\n- total_entry_events (top level) = grand total of entry events across ALL persons combined.\\n- total_exit_events (top level) = grand total of exit events across ALL persons combined.\\n\\n\\u2550\\u2550\\u2550 DIRECTION SUMMARY \\u2550\\u2550\\u2550\\n\\n- \\\"entered_only\\\" \\u2014 only entered, never exited (one or more entry events, zero exits)\\n- \\\"exited_only\\\" \\u2014 only exited, never entered (one or more exits, zero entries; person was inside at start)\\n- \\\"entered_then_exited\\\" \\u2014 exactly one entry followed by exactly one exit\\n- \\\"exited_then_entered\\\" \\u2014 exactly one exit followed by exactly one entry\\n- \\\"multiple_visits\\\" \\u2014 three or more total events (any mixed pattern of multiple entries and exits)\\n- \\\"passing_by\\\" \\u2014 never actually crossed the door (events array is empty)\\n\\n\\u2550\\u2550\\u2550 OUTPUT SCHEMA \\u2550\\u2550\\u2550\\n\\n{\\n \\\"video_duration_seconds\\\": ,\\n \\\"total_unique_persons\\\": ,\\n \\\"total_entry_events\\\": ,\\n \\\"total_exit_events\\\": ,\\n \\\"persons\\\": [\\n {\\n \\\"person_id\\\": \\\"P1\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"first_seen_seconds\\\": ,\\n \\\"last_seen_seconds\\\": ,\\n \\\"events\\\": [\\n { \\\"t\\\": , \\\"type\\\": \\\"entry\\\" | \\\"exit\\\" }\\n ],\\n \\\"total_dwell_seconds\\\": ,\\n \\\"direction_summary\\\": \\\"entered_only\\\" | \\\"exited_only\\\" | \\\"entered_then_exited\\\" | \\\"exited_then_entered\\\" | \\\"multiple_visits\\\" | \\\"passing_by\\\",\\n \\\"estimated_gender\\\": \\\"male\\\" | \\\"female\\\" | \\\"unknown\\\",\\n \\\"gender_confidence\\\": <0.0-1.0>,\\n \\\"estimated_age_bracket\\\": \\\"0-12\\\" | \\\"13-17\\\" | \\\"18-24\\\" | \\\"25-34\\\" | \\\"35-44\\\" | \\\"45-54\\\" | \\\"55-64\\\" | \\\"65+\\\" | \\\"unknown\\\",\\n \\\"age_confidence\\\": <0.0-1.0>,\\n \\\"notes\\\": \\\"\\\"\\n }\\n ],\\n \\\"frames\\\": [\\n {\\n \\\"t\\\": 0,\\n \\\"persons\\\": [\\n { \\\"person_id\\\": \\\"P1\\\", \\\"position\\\": [, ] }\\n ]\\n }\\n ]\\n}\\n\\n\\u2550\\u2550\\u2550 FINAL RULES \\u2550\\u2550\\u2550\\n\\n- Sample at 1-second intervals starting at t=0. Include t=0 if anyone is clearly visible there. Omit any frame where no one is clearly visible.\\n- Report timing as it actually happens \\u2014 do not shift, delay, or smooth timestamps.\\n- A person who walks past without crossing the door has an empty `events` array and direction_summary \\\"passing_by\\\".\\n- Output ONLY the JSON object. No prose, no markdown fences, no commentary.\";\n","deleted",false,"label",[37],"54646446-4168-11f1-829f-42b51c65c3df","main.tsx","9afc1298-40fd-11f1-a6e0-42b51c65c3df",20,["D",1777205177313],17,"http","const HTML_TEMPLATE = `\n\n\n\n\nStore Intelligence — Visual Analysis\n\n\n\n\n\n\n\n\n\n

\n \n\n

\n\n

Settings — Gemini API

\n Gemini API Key\n

\n \n \n \n Stored only in your browser. Sent only to generativelanguage.googleapis.com.\n

\n Model\n \n

\n Prompt\n \n

\n \n Edits are saved in your browser.\n

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JSON

\n \n

\n \n \n \n

\n\n \n

\n\n

\n\n\n\n\n\n`;\n\nimport EXAMPLES from \"./examples.tsx\";\nimport PROMPT from \"./prompt.tsx\";\nconst DEFAULT_INDEX = 0;\n\nconst HTML = HTML_TEMPLATE\n .replace(\"__EXAMPLES_PLACEHOLDER__\", JSON.stringify(EXAMPLES))\n .replace(\"__DEFAULT_INDEX_PLACEHOLDER__\", String(DEFAULT_INDEX))\n .replace(\"__PROMPT_PLACEHOLDER__\", JSON.stringify(PROMPT));\n\nexport default async function (req: Request): Promise {\n return new Response(HTML, {\n headers: {\n \"Content-Type\": \"text/html; charset=utf-8\",\n \"Cache-Control\": \"public, max-age=60\"\n }\n });\n}\n",[59],"53b863e4-4168-11f1-be11-42b51c65c3df","examples.tsx","53b864b6-4168-11f1-be11-42b51c65c3df",18,["D",1777205176206],"export default [{\"name\":\"Store Entrance\",\"video_url\":\"https://videos.pexels.com/video-files/6641527/6641527-hd_1920_1080_30fps.mp4\",\"data\":{\"video_duration_seconds\":20,\"total_unique_persons\":5,\"total_entry_events\":4,\"total_exit_events\":1,\"persons\":[{\"person_id\":\"P1\",\"description\":\"Male, wearing a backwards purple baseball cap, red and white plaid shirt over a dark t-shirt, dark baggy pants, and grey slip-on shoes.\",\"first_seen_seconds\":0,\"last_seen_seconds\":2,\"entry_timestamp_seconds\":2,\"exit_timestamp_seconds\":null,\"dwell_seconds\":null,\"direction_summary\":\"entered_only\",\"estimated_gender\":\"male\",\"gender_confidence\":0.9,\"estimated_age_bracket\":\"18-24\",\"age_confidence\":0.8,\"notes\":\"Calibration: door spans approximately x=290 to x=520, y=260 to y=900.\"},{\"person_id\":\"P2\",\"description\":\"Male, grey hair, wearing glasses, blue and black plaid button-down shirt, blue jeans, and dark shoes.\",\"first_seen_seconds\":0,\"last_seen_seconds\":4,\"entry_timestamp_seconds\":4,\"exit_timestamp_seconds\":null,\"dwell_seconds\":null,\"direction_summary\":\"entered_only\",\"estimated_gender\":\"male\",\"gender_confidence\":0.9,\"estimated_age_bracket\":\"55-64\",\"age_confidence\":0.8,\"notes\":\"\"},{\"person_id\":\"P3\",\"description\":\"Male, grey hair visible under a dark green baseball cap, wearing a black jacket, blue jeans, white sneakers, and a light-colored face mask.\",\"first_seen_seconds\":1,\"last_seen_seconds\":13,\"entry_timestamp_seconds\":12,\"exit_timestamp_seconds\":null,\"dwell_seconds\":null,\"direction_summary\":\"entered_only\",\"estimated_gender\":\"male\",\"gender_confidence\":0.8,\"estimated_age_bracket\":\"65+\",\"age_confidence\":0.6,\"notes\":\"\"},{\"person_id\":\"P4\",\"description\":\"Female, long brown hair, wearing a green quilted puffer jacket, blue jeans, brown knee-high boots, a face mask, and carrying a brown leather shoulder bag.\",\"first_seen_seconds\":9,\"last_seen_seconds\":14,\"entry_timestamp_seconds\":13,\"exit_timestamp_seconds\":null,\"dwell_seconds\":null,\"direction_summary\":\"entered_only\",\"estimated_gender\":\"female\",\"gender_confidence\":0.9,\"estimated_age_bracket\":\"25-34\",\"age_confidence\":0.6,\"notes\":\"\"},{\"person_id\":\"P5\",\"description\":\"Male, balding with grey hair, wearing a light blue face mask, dark vest with a yellow upper back and shoulders over a light blue long-sleeve shirt, blue jeans, and brown shoes.\",\"first_seen_seconds\":16,\"last_seen_seconds\":19,\"entry_timestamp_seconds\":null,\"exit_timestamp_seconds\":16,\"dwell_seconds\":null,\"direction_summary\":\"exited_only\",\"estimated_gender\":\"male\",\"gender_confidence\":0.9,\"estimated_age_bracket\":\"65+\",\"age_confidence\":0.8,\"notes\":\"\"}],\"frames\":[{\"t\":0,\"persons\":[{\"person_id\":\"P1\",\"position\":[40,580]},{\"person_id\":\"P2\",\"position\":[10,550]}]},{\"t\":1,\"persons\":[{\"person_id\":\"P1\",\"position\":[220,580]},{\"person_id\":\"P2\",\"position\":[100,550]},{\"person_id\":\"P3\",\"position\":[10,470]}]},{\"t\":2,\"persons\":[{\"person_id\":\"P1\",\"position\":[410,550]},{\"person_id\":\"P2\",\"position\":[170,550]},{\"person_id\":\"P3\",\"position\":[10,480]}]},{\"t\":3,\"persons\":[{\"person_id\":\"P2\",\"position\":[310,550]},{\"person_id\":\"P3\",\"position\":[15,490]}]},{\"t\":4,\"persons\":[{\"person_id\":\"P2\",\"position\":[460,470]},{\"person_id\":\"P3\",\"position\":[20,500]}]},{\"t\":5,\"persons\":[{\"person_id\":\"P3\",\"position\":[30,510]}]},{\"t\":6,\"persons\":[{\"person_id\":\"P3\",\"position\":[40,510]}]},{\"t\":7,\"persons\":[{\"person_id\":\"P3\",\"position\":[60,510]}]},{\"t\":8,\"persons\":[{\"person_id\":\"P3\",\"position\":[90,510]}]},{\"t\":9,\"persons\":[{\"person_id\":\"P3\",\"position\":[120,510]},{\"person_id\":\"P4\",\"position\":[20,600]}]},{\"t\":10,\"persons\":[{\"person_id\":\"P3\",\"position\":[160,510]},{\"person_id\":\"P4\",\"position\":[80,600]}]},{\"t\":11,\"persons\":[{\"person_id\":\"P3\",\"position\":[220,510]},{\"person_id\":\"P4\",\"position\":[150,600]}]},{\"t\":12,\"persons\":[{\"person_id\":\"P3\",\"position\":[330,510]},{\"person_id\":\"P4\",\"position\":[250,600]}]},{\"t\":13,\"persons\":[{\"person_id\":\"P3\",\"position\":[420,490]},{\"person_id\":\"P4\",\"position\":[380,580]}]},{\"t\":14,\"persons\":[{\"person_id\":\"P4\",\"position\":[450,500]}]},{\"t\":16,\"persons\":[{\"person_id\":\"P5\",\"position\":[420,480]}]},{\"t\":17,\"persons\":[{\"person_id\":\"P5\",\"position\":[260,510]}]},{\"t\":18,\"persons\":[{\"person_id\":\"P5\",\"position\":[120,520]}]},{\"t\":19,\"persons\":[{\"person_id\":\"P5\",\"position\":[20,520]}]}]}},{\"name\":\"Walking Out and Back In\",\"video_url\":\"https://files.catbox.moe/fqlsqe.mp4\",\"data\":{\"video_duration_seconds\":45,\"total_unique_persons\":1,\"total_entry_events\":1,\"total_exit_events\":1,\"persons\":[{\"person_id\":\"P1\",\"description\":\"Male wearing a black t-shirt with a purple 'Dega.ai' logo on the back, dark jeans, and glasses. Dark curly hair and a beard, holding a smartphone.\",\"first_seen_seconds\":0,\"last_seen_seconds\":44,\"entry_timestamp_seconds\":39,\"exit_timestamp_seconds\":35,\"dwell_seconds\":null,\"direction_summary\":\"exited_then_entered\",\"estimated_gender\":\"male\",\"gender_confidence\":0.95,\"estimated_age_bracket\":\"25-34\",\"age_confidence\":0.85,\"notes\":\"Calibration: door spans approximately x=200 to x=800, y=100 to y=400.\"}],\"frames\":[{\"t\":0,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":1,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":2,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":3,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":4,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":5,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":6,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":7,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":8,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":9,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":10,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":11,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":12,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":13,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":14,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":15,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":16,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":17,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":18,\"persons\":[{\"person_id\":\"P1\",\"position\":[600,650]}]},{\"t\":19,\"persons\":[{\"person_id\":\"P1\",\"position\":[600,650]}]},{\"t\":20,\"persons\":[{\"person_id\":\"P1\",\"position\":[600,650]}]},{\"t\":21,\"persons\":[{\"person_id\":\"P1\",\"position\":[600,650]}]},{\"t\":22,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,600]}]},{\"t\":23,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":24,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":25,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":26,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":27,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":28,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,500]}]},{\"t\":29,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,450]}]},{\"t\":30,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,450]}]},{\"t\":31,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,450]}]},{\"t\":32,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,450]}]},{\"t\":33,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,450]}]},{\"t\":34,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,400]}]},{\"t\":35,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,350]}]},{\"t\":38,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,350]}]},{\"t\":39,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,400]}]},{\"t\":40,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,450]}]},{\"t\":41,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,500]}]},{\"t\":42,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,550]}]},{\"t\":43,\"persons\":[{\"person_id\":\"P1\",\"position\":[550,600]}]},{\"t\":44,\"persons\":[{\"person_id\":\"P1\",\"position\":[300,700]}]}]}},{\"name\":\"Two-Person Visit\",\"video_url\":\"https://files.catbox.moe/b5rb85.mp4\",\"data\":{\"video_duration_seconds\":59,\"total_unique_persons\":2,\"total_entry_events\":2,\"total_exit_events\":2,\"persons\":[{\"person_id\":\"P1\",\"description\":\"Male, black t-shirt with small logo on left chest, blue jeans, glasses, beard, dark curly hair.\",\"first_seen_seconds\":0,\"last_seen_seconds\":58,\"entry_timestamp_seconds\":24,\"exit_timestamp_seconds\":26,\"dwell_seconds\":2,\"direction_summary\":\"entered_then_exited\",\"estimated_gender\":\"male\",\"gender_confidence\":0.95,\"estimated_age_bracket\":\"25-34\",\"age_confidence\":0.85,\"notes\":\"Calibration: door spans approximately x=200 to x=800, y=0 to y=150. 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