> ## Documentation Index > Fetch the complete documentation index at: https://docs.halfpagetechnologies.com/llms.txt > Use this file to discover all available pages before exploring further. # Export project measurements (CSV) > Download per-cell measurements for a whole project as a single CSV — every analyzed image in the project, one row per detected ROI. Each image contributes its most recent completed segmentation; images that have never been analyzed contribute no rows. Use the `image_name` and `segmentation_id` columns to split the file back out per image. Returned as a file attachment. ## OpenAPI ````yaml /api-reference/openapi.json get /api/v1/export/project/{folder_id}/measurements.csv openapi: 3.1.0 info: title: HalfPage API description: > The **HalfPage API** runs Cellpose-based cell segmentation on your microscopy images programmatically — upload an image, run a prediction, and export the resulting cell masks, ROIs, and measurements. ## Base URL ``` https://api.halfpagetechnologies.com/backend/api/v1 ``` Every path in this reference is relative to that base URL. A staging environment is available at `https://staging-api.halfpagetechnologies.com/backend/api/v1`. ## Authentication Authenticate every request with an API key in the `Authorization` header as a bearer token: ``` Authorization: Bearer hp_live_xxxxxxxxxxxxxxxxxxxxxxxx ``` Create and manage keys from the **API Keys** section of the HalfPage dashboard — keys cannot be minted through the API. A key is scoped to the organization that owns it; every resource you create or read is confined to that organization. Requests without a valid key receive `401`; a valid key used against an endpoint outside the public product surface receives `403`. ## Core workflow Segmentation runs asynchronously on GPU workers, so the API is poll-based: 1. **Upload an image** (`POST /upload`) as `multipart/form-data` with a `file` field. One call: the image record is created automatically and the response returns it already `ready`, with the `image_id` for the next step. *(Large files: `POST /image` with `resumable: true` returns presigned part URLs — PUT the chunks, call `POST /upload/{image_id}/complete`, then poll `GET /image/{image_id}` until `upload_status` is `ready`.)* 2. **Pick a model** (`GET /models`) and **submit a prediction** (`POST /job/prediction`) with the `image_id` and a `model_id`. This returns a `job_id`. 3. **Poll the job** (`GET /job/{job_id}`) until `status` is `COMPLETED`; the response then carries a `segmentation_id`. 4. **Export** results for that segmentation as CSV measurements (`GET /export/{segmentation_id}/measurements.csv`), GeoJSON ROIs (`.../rois.geojson`), or an ImageJ ROI archive (`.../rois.zip`). To collect a whole project at once, `GET /export/project/{folder_id}/measurements.csv` returns every analyzed image in it as one CSV. ## Quotas Uploads and analyses are metered against your plan. Exceeding your image storage cap or monthly analysis cap returns `402` with a human-readable `detail` explaining the limit — upgrade your plan to raise it. version: 1.0.0 servers: - url: https://api.halfpagetechnologies.com/backend description: Production - url: https://staging-api.halfpagetechnologies.com/backend description: Staging security: [] tags: - name: upload description: >- Get microscopy images into HalfPage. The single-request `POST /upload` is the simplest path: it creates the image record for you and returns it `ready`. For large files over flaky connections, use the resumable flow: `POST /image` with `resumable: true` (or `init`), PUT the presigned parts, then `complete` (no ETag bookkeeping needed) and poll the image until `ready`. - name: job description: >- Submit and poll asynchronous GPU jobs. `POST /job/prediction` runs segmentation on a ready image; `POST /job/training` fine-tunes a custom model. Both return a `job_id` you poll via `GET /job/{job_id}` until it is `COMPLETED`. - name: image description: >- Create and manage image records. `POST /image` starts the resumable upload flow (with `resumable: true` it returns presigned part URLs in the same call); poll `GET /image/{image_id}` for the upload lifecycle (`uploading` → `processing` → `ready`) and metadata such as pixel `shape`, and delete images you no longer need. - name: segmentation description: >- Read the segmentations produced by prediction jobs and derive new ones. A segmentation is the set of detected cell ROIs for an image; fetch its metadata, regenerate its mask from annotations, or fork a refinement to iterate on the result. - name: export description: >- Download results in analysis-ready formats: a CSV of per-cell measurements, a GeoJSON of ROI polygons, or a ZIP of ImageJ-compatible ROIs. Export one segmentation at a time, or pull every analyzed image in a project into a single CSV. Both CSV endpoints accept a `columns` parameter to narrow the output to the measurements you care about. - name: models description: >- List the segmentation models available to your organization — the shared public base models (e.g. `cpsam`) plus any custom models you have trained — to choose a `model_id` for a prediction job. paths: /api/v1/export/project/{folder_id}/measurements.csv: get: tags: - export summary: Export project measurements (CSV) description: >- Download per-cell measurements for a whole project as a single CSV — every analyzed image in the project, one row per detected ROI. Each image contributes its most recent completed segmentation; images that have never been analyzed contribute no rows. Use the `image_name` and `segmentation_id` columns to split the file back out per image. Returned as a file attachment. operationId: >- export_project_measurements_csv_api_v1_export_project__folder_id__measurements_csv_get parameters: - name: folder_id in: path required: true schema: type: string format: uuid title: Folder Id - name: columns in: query required: false schema: anyOf: - type: array items: type: string - type: 'null' description: >- Measurement columns to include, repeated once per column. Omit for all columns. Valid columns: image_name, image_width, image_height, segmentation_id, segmentation_source, model_name, confluency, roi_index, roi_id, cell_type, tags, entity_type, area_px, perimeter_px, centroid_x, centroid_y, bbox_x, bbox_y, bbox_w, bbox_h. examples: - - image_name - roi_id - cell_type - area_px title: Columns description: >- Measurement columns to include, repeated once per column. Omit for all columns. Valid columns: image_name, image_width, image_height, segmentation_id, segmentation_source, model_name, confluency, roi_index, roi_id, cell_type, tags, entity_type, area_px, perimeter_px, centroid_x, centroid_y, bbox_x, bbox_y, bbox_w, bbox_h. responses: '200': description: >- A CSV of per-cell measurements for every analyzed image in the project. content: text/csv: schema: type: string format: binary '400': description: The `columns` selection names a column this export does not produce. content: application/json: example: detail: >- Unknown export column(s): area_um. Valid columns are: image_name, image_width, ... schema: $ref: '#/components/schemas/ErrorResponse' '401': description: >- Missing or invalid credentials. Supply a valid `Authorization: Bearer hp_live_...` API key. content: application/json: example: detail: Requires authentication schema: $ref: '#/components/schemas/ErrorResponse' '404': description: The resource does not exist, or is not in your organization. content: application/json: example: detail: Image with id 3fa85f64-5717-4562-b3fc-2c963f66afa6 not found schema: $ref: '#/components/schemas/ErrorResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: ErrorResponse: properties: detail: type: string title: Detail examples: - Job with id 11111111-1111-1111-1111-111111111111 not found type: object required: - detail title: ErrorResponse description: |- The body returned for a handled error: a single human-readable ``detail`` string. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type input: title: Input ctx: type: object title: Context type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http scheme: bearer ````