CSV Formatter¶
Class: CSVFormatterBlockV1
Source: inference.core.workflows.core_steps.formatters.csv.v1.CSVFormatterBlockV1
The CSV Formatter block prepares structured CSV content based on specified data configurations within a workflow. It allows users to:
-
choose which data appears as columns
-
apply operations to transform the data within the block
-
aggregate whole batch of data into single CSV document (see Data Aggregation section)
The generated CSV content can be used as input for other blocks, such as File Sink or Email Notifications.
Defining columns¶
Use columns_data
property to specify name of the columns and data sources. Defining UQL operations in
columns_operations
you can perform specific operation on each column.
Timestamp column
The block automatically adds timestamp
column and this column name is reserved and cannot be used.
The value of timestamp would be in the following format: 2024-10-18T14:09:57.622297+00:00
, values
are scaled to UTC time zone.
For example, the following definition
columns_data = {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names",
}
columns_operations = {
"predictions": [
{"type": "DetectionsPropertyExtract", "property_name": "class_name"}
],
}
Will generate CSV content:
timestamp,predictions,reference
"2024-10-16T11:15:15.336322+00:00","['a', 'b', 'c']","['a', 'b']"
When applied on object detection predictions from a single image, assuming that $inputs.reference_class_names
holds a list of reference classes.
Data Aggregation¶
The block may take input from different blocks, hence its behavior may differ depending on context:
-
data
batch_size=1
: whenever single input is provided - block will provide the output as in the example above - CSV header will be placed in the first row, the second row will hold the data -
data
batch_size>1
: each datapoint will create one row in CSV document, but only the last batch element will be fed with the aggregated output, leaving other batch elements' outputs empty
When should I expect batch_size=1
?¶
You may expect batch_size=1
in the following scenarios:
-
CSV Formatter was connected to the output of block that only operates on one image and produces one prediction
-
CSV Formatter was connected to the output of block that aggregates data for whole batch and produces single non-empty output (which is exactly the characteristics of CSV Formatter itself)
When should I expect batch_size>1
?¶
You may expect batch_size=1
in the following scenarios:
- CSV Formatter was connected to the output of block that produces single prediction for single image, but batch of images were fed - then CSV Formatter will aggregate the CSV content and output it in the position of the last batch element:
--- input_batch[0] ----> ┌───────────────────────┐ ----> <Empty>
--- input_batch[1] ----> │ │ ----> <Empty>
... │ CSV Formatter │ ----> <Empty>
... │ │ ----> <Empty>
--- input_batch[n] ----> └───────────────────────┘ ----> {"csv_content": "..."}
Format of CSV document for batch_size>1
If the example presented above is applied for larger input batch sizes - the output document structure would be as follows:
timestamp,predictions,reference
"2024-10-16T11:15:15.336322+00:00","['a', 'b', 'c']","['a', 'b']"
"2024-10-16T11:15:15.436322+00:00","['b', 'c']","['a', 'b']"
"2024-10-16T11:15:15.536322+00:00","['a', 'c']","['a', 'b']"
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/csv_formatter@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
columns_data |
Dict[str, Union[bool, float, int, str]] |
References data to be used to construct each and every column. | ✅ |
columns_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. each column. | ❌ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to CSV Formatter
in version v1
.
- inputs:
Keypoint Detection Model
,Detections Filter
,Rate Limiter
,VLM as Classifier
,Data Aggregator
,Blur Visualization
,Circle Visualization
,Pixelate Visualization
,YOLO-World Model
,Model Comparison Visualization
,Webhook Sink
,Detections Transformation
,Time in Zone
,Time in Zone
,Grid Visualization
,Dominant Color
,Llama 3.2 Vision
,Image Contours
,Label Visualization
,Florence-2 Model
,Dynamic Crop
,Triangle Visualization
,Property Definition
,Florence-2 Model
,QR Code Detection
,Multi-Label Classification Model
,Corner Visualization
,Line Counter Visualization
,LMM For Classification
,CLIP Embedding Model
,Size Measurement
,Anthropic Claude
,Roboflow Dataset Upload
,First Non Empty Or Default
,Cache Set
,Distance Measurement
,Roboflow Custom Metadata
,Polygon Zone Visualization
,Stitch Images
,Image Slicer
,Relative Static Crop
,Segment Anything 2 Model
,Google Gemini
,SIFT Comparison
,Dimension Collapse
,Buffer
,Image Slicer
,Image Threshold
,Camera Focus
,Qwen2.5-VL
,Expression
,JSON Parser
,Overlap Filter
,Path Deviation
,Environment Secrets Store
,Color Visualization
,Barcode Detection
,OpenAI
,Clip Comparison
,Roboflow Dataset Upload
,Mask Visualization
,CSV Formatter
,Stitch OCR Detections
,Delta Filter
,Detections Stabilizer
,Moondream2
,Detection Offset
,Multi-Label Classification Model
,Line Counter
,Detections Merge
,Absolute Static Crop
,Identify Changes
,Single-Label Classification Model
,Crop Visualization
,Ellipse Visualization
,Stability AI Inpainting
,Stability AI Image Generation
,Keypoint Detection Model
,Cache Get
,Stability AI Outpainting
,Detections Consensus
,Single-Label Classification Model
,Bounding Box Visualization
,Path Deviation
,Cosine Similarity
,VLM as Classifier
,Identify Outliers
,Background Color Visualization
,LMM
,Object Detection Model
,Twilio SMS Notification
,Detections Stitch
,VLM as Detector
,Instance Segmentation Model
,Keypoint Visualization
,OCR Model
,Halo Visualization
,PTZ Tracking (ONVIF)
.md),SmolVLM2
,Time in Zone
,Perception Encoder Embedding Model
,Template Matching
,Continue If
,Dot Visualization
,Clip Comparison
,Slack Notification
,Icon Visualization
,Depth Estimation
,CogVLM
,Byte Tracker
,OpenAI
,Email Notification
,Detections Classes Replacement
,SIFT Comparison
,Line Counter
,Byte Tracker
,Pixel Color Count
,Gaze Detection
,Perspective Correction
,Byte Tracker
,Camera Calibration
,Classification Label Visualization
,Model Monitoring Inference Aggregator
,Instance Segmentation Model
,Local File Sink
,Reference Path Visualization
,Velocity
,Bounding Rectangle
,QR Code Generator
,OpenAI
,SIFT
,Polygon Visualization
,Image Convert Grayscale
,VLM as Detector
,Dynamic Zone
,Image Blur
,Trace Visualization
,Google Vision OCR
,Image Preprocessing
,Object Detection Model
- outputs:
Crop Visualization
,Ellipse Visualization
,Stability AI Inpainting
,Stability AI Image Generation
,Cache Get
,Circle Visualization
,YOLO-World Model
,Model Comparison Visualization
,Webhook Sink
,Stability AI Outpainting
,Bounding Box Visualization
,Time in Zone
,Time in Zone
,Path Deviation
,Background Color Visualization
,Llama 3.2 Vision
,LMM
,Twilio SMS Notification
,Label Visualization
,Triangle Visualization
,Dynamic Crop
,Florence-2 Model
,Detections Stitch
,Instance Segmentation Model
,Florence-2 Model
,Keypoint Visualization
,Halo Visualization
,PTZ Tracking (ONVIF)
.md),Corner Visualization
,Line Counter Visualization
,LMM For Classification
,CLIP Embedding Model
,Time in Zone
,Perception Encoder Embedding Model
,Dot Visualization
,Size Measurement
,Anthropic Claude
,Slack Notification
,Roboflow Dataset Upload
,Icon Visualization
,Cache Set
,Roboflow Custom Metadata
,Distance Measurement
,CogVLM
,Polygon Zone Visualization
,OpenAI
,Email Notification
,Segment Anything 2 Model
,Detections Classes Replacement
,Google Gemini
,SIFT Comparison
,Line Counter
,Pixel Color Count
,Image Threshold
,Perspective Correction
,Classification Label Visualization
,Path Deviation
,Reference Path Visualization
,Local File Sink
,OpenAI
,Instance Segmentation Model
,Color Visualization
,Model Monitoring Inference Aggregator
,Clip Comparison
,Roboflow Dataset Upload
,Mask Visualization
,QR Code Generator
,OpenAI
,Polygon Visualization
,Moondream2
,Image Blur
,Trace Visualization
,Line Counter
,Google Vision OCR
,Image Preprocessing
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
CSV Formatter
in version v1
has.
Bindings
Example JSON definition of step CSV Formatter
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/csv_formatter@v1",
"columns_data": {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names"
},
"columns_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
}
}