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:
Camera Calibration
,Stitch OCR Detections
,CSV Formatter
,Instance Segmentation Model
,Cosine Similarity
,CogVLM
,Absolute Static Crop
,Distance Measurement
,Object Detection Model
,Florence-2 Model
,Byte Tracker
,Label Visualization
,Path Deviation
,Single-Label Classification Model
,Dimension Collapse
,Detections Stitch
,Model Comparison Visualization
,Slack Notification
,Object Detection Model
,VLM as Classifier
,Bounding Rectangle
,Detections Consensus
,Data Aggregator
,Detection Offset
,Halo Visualization
,Overlap Filter
,Keypoint Detection Model
,Time in Zone
,First Non Empty Or Default
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Detections Transformation
,Camera Focus
,Delta Filter
,Detections Classes Replacement
,VLM as Detector
,Cache Set
,Local File Sink
,Path Deviation
,OpenAI
,Google Vision OCR
,VLM as Detector
,Stability AI Image Generation
,Ellipse Visualization
,Blur Visualization
,Circle Visualization
,Image Blur
,Background Color Visualization
,Multi-Label Classification Model
,Single-Label Classification Model
,Rate Limiter
,Detections Filter
,Pixelate Visualization
,Identify Outliers
,Google Gemini
,Stability AI Inpainting
,Keypoint Detection Model
,Keypoint Visualization
,Template Matching
,Mask Visualization
,SmolVLM2
,YOLO-World Model
,Trace Visualization
,JSON Parser
,Polygon Visualization
,Segment Anything 2 Model
,Expression
,LMM
,Depth Estimation
,Classification Label Visualization
,LMM For Classification
,Clip Comparison
,Line Counter
,Stitch Images
,Byte Tracker
,Image Slicer
,Roboflow Dataset Upload
,Multi-Label Classification Model
,Twilio SMS Notification
,SIFT Comparison
,OpenAI
,QR Code Detection
,Environment Secrets Store
,Qwen2.5-VL
,Roboflow Dataset Upload
,Bounding Box Visualization
,Llama 3.2 Vision
,Pixel Color Count
,Moondream2
,Grid Visualization
,Detections Merge
,Continue If
,Dynamic Zone
,Image Convert Grayscale
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Cache Get
,Identify Changes
,Florence-2 Model
,Size Measurement
,SIFT
,Dot Visualization
,CLIP Embedding Model
,Gaze Detection
,Color Visualization
,Byte Tracker
,Dominant Color
,Clip Comparison
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,Property Definition
,Detections Stabilizer
,OCR Model
,Velocity
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,Instance Segmentation Model
,Corner Visualization
,Barcode Detection
,Crop Visualization
,Email Notification
,Image Contours
,Image Slicer
,Line Counter
,Image Threshold
- outputs:
LMM
,Classification Label Visualization
,LMM For Classification
,Clip Comparison
,Line Counter
,Instance Segmentation Model
,CogVLM
,Roboflow Dataset Upload
,Distance Measurement
,Twilio SMS Notification
,Florence-2 Model
,OpenAI
,Label Visualization
,Path Deviation
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Pixel Color Count
,Halo Visualization
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Time in Zone
,Cache Get
,Florence-2 Model
,Cache Set
,Local File Sink
,Path Deviation
,OpenAI
,Google Vision OCR
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,Circle Visualization
,Dot Visualization
,Image Blur
,CLIP Embedding Model
,Background Color Visualization
,Color Visualization
,Google Gemini
,Stability AI Inpainting
,Polygon Zone Visualization
,Keypoint Visualization
,Mask Visualization
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,YOLO-World Model
,Trace Visualization
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Crop Visualization
,Email Notification
,Line Counter
,Image Threshold
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"
}
]
}
}