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