-
Notifications
You must be signed in to change notification settings - Fork 130
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #765 from roboflow/stitch_ocr_detections_workflow_…
…block Stitch ocr detections workflow block
- Loading branch information
Showing
13 changed files
with
709 additions
and
80 deletions.
There are no files selected for viewing
Large diffs are not rendered by default.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,4 @@ | ||
| __version__ = "0.24.0" | ||
| __version__ = "0.25.0" | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
|
|
||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Empty file.
294 changes: 294 additions & 0 deletions
294
inference/core/workflows/core_steps/transformations/stitch_ocr_detections/v1.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,294 @@ | ||
| from enum import Enum | ||
| from typing import Dict, List, Literal, Optional, Tuple, Type, Union | ||
|
|
||
| import numpy as np | ||
| import supervision as sv | ||
| from pydantic import AliasChoices, ConfigDict, Field, field_validator | ||
|
|
||
| from inference.core.workflows.execution_engine.entities.base import ( | ||
| Batch, | ||
| OutputDefinition, | ||
| ) | ||
| from inference.core.workflows.execution_engine.entities.types import ( | ||
| INTEGER_KIND, | ||
| OBJECT_DETECTION_PREDICTION_KIND, | ||
| STRING_KIND, | ||
| StepOutputSelector, | ||
| WorkflowParameterSelector, | ||
| ) | ||
| from inference.core.workflows.prototypes.block import ( | ||
| BlockResult, | ||
| WorkflowBlock, | ||
| WorkflowBlockManifest, | ||
| ) | ||
|
|
||
| LONG_DESCRIPTION = """ | ||
| Combines OCR detection results into a coherent text string by organizing detections spatially. | ||
| This transformation is perfect for turning individual OCR results into structured, readable text! | ||
| #### How It Works | ||
| This transformation reconstructs the original text from OCR detection results by: | ||
| 1. 📐 **Grouping** text detections into rows based on their vertical (`y`) positions | ||
| 2. 📏 **Sorting** detections within each row by horizontal (`x`) position | ||
| 3. 📜 **Concatenating** the text in reading order (left-to-right, top-to-bottom) | ||
| #### Parameters | ||
| - **`tolerance`**: Controls how close detections need to be vertically to be considered part of the same line of text. | ||
| A higher tolerance will group detections that are further apart vertically. | ||
| - **`reading_direction`**: Determines the order in which text is read. Available options: | ||
| * **"left_to_right"**: Standard left-to-right reading (e.g., English) ➡️ | ||
| * **"right_to_left"**: Right-to-left reading (e.g., Arabic) ⬅️ | ||
| * **"vertical_top_to_bottom"**: Vertical reading from top to bottom ⬇️ | ||
| * **"vertical_bottom_to_top"**: Vertical reading from bottom to top ⬆️ | ||
| #### Why Use This Transformation? | ||
| This is especially useful for: | ||
| - 📖 Converting individual character/word detections into a readable text block | ||
| - 📝 Reconstructing multi-line text from OCR results | ||
| - 🔀 Maintaining proper reading order for detected text elements | ||
| - 🌏 Supporting different writing systems and text orientations | ||
| #### Example Usage | ||
| Use this transformation after an OCR model that outputs individual words or characters, so you can reconstruct the | ||
| original text layout in its intended format. | ||
| """ | ||
|
|
||
| SHORT_DESCRIPTION = "Combines OCR detection results into a coherent text string by organizing detections spatially." | ||
|
|
||
|
|
||
| class ReadingDirection(str, Enum): | ||
| LEFT_TO_RIGHT = "left_to_right" | ||
| RIGHT_TO_LEFT = "right_to_left" | ||
| VERTICAL_TOP_TO_BOTTOM = "vertical_top_to_bottom" | ||
| VERTICAL_BOTTOM_TO_TOP = "vertical_bottom_to_top" | ||
|
|
||
|
|
||
| class BlockManifest(WorkflowBlockManifest): | ||
| model_config = ConfigDict( | ||
| json_schema_extra={ | ||
| "name": "Stitch OCR Detections", | ||
| "version": "v1", | ||
| "short_description": SHORT_DESCRIPTION, | ||
| "long_description": LONG_DESCRIPTION, | ||
| "license": "Apache-2.0", | ||
| "block_type": "transformation", | ||
| "ui_manifest": { | ||
| "section": "advanced", | ||
| "icon": "fal fa-reel", | ||
| "blockPriority": 2, | ||
| }, | ||
| } | ||
| ) | ||
| type: Literal["roboflow_core/stitch_ocr_detections@v1"] | ||
| predictions: StepOutputSelector( | ||
| kind=[ | ||
| OBJECT_DETECTION_PREDICTION_KIND, | ||
| ] | ||
| ) = Field( | ||
| title="OCR Detections", | ||
| description="The output of an OCR detection model.", | ||
| examples=["$steps.my_ocr_detection_model.predictions"], | ||
| ) | ||
| reading_direction: Literal[ | ||
| "left_to_right", | ||
| "right_to_left", | ||
| "vertical_top_to_bottom", | ||
| "vertical_bottom_to_top", | ||
| ] = Field( | ||
| title="Reading Direction", | ||
| description="The direction of the text in the image.", | ||
| examples=["right_to_left"], | ||
| json_schema_extra={ | ||
| "values_metadata": { | ||
| "left_to_right": { | ||
| "name": "Left To Right", | ||
| "description": "Standard left-to-right reading (e.g., English language)", | ||
| }, | ||
| "right_to_left": { | ||
| "name": "Right To Left", | ||
| "description": "Right-to-left reading (e.g., Arabic)", | ||
| }, | ||
| "vertical_top_to_bottom": { | ||
| "name": "Top To Bottom (Vertical)", | ||
| "description": "Vertical reading from top to bottom", | ||
| }, | ||
| "vertical_bottom_to_top": { | ||
| "name": "Bottom To Top (Vertical)", | ||
| "description": "Vertical reading from bottom to top", | ||
| }, | ||
| } | ||
| }, | ||
| ) | ||
| tolerance: Union[int, WorkflowParameterSelector(kind=[INTEGER_KIND])] = Field( | ||
| title="Tolerance", | ||
| description="The tolerance for grouping detections into the same line of text.", | ||
| default=10, | ||
| examples=[10, "$inputs.tolerance"], | ||
| ) | ||
|
|
||
| @field_validator("tolerance") | ||
| @classmethod | ||
| def ensure_tolerance_greater_than_zero( | ||
| cls, value: Union[int, str] | ||
| ) -> Union[int, str]: | ||
| if isinstance(value, int) and value <= 0: | ||
| raise ValueError( | ||
| "Stitch OCR detections block expects `tollerance` to be greater than zero." | ||
| ) | ||
| return value | ||
|
|
||
| @classmethod | ||
| def accepts_batch_input(cls) -> bool: | ||
| return True | ||
|
|
||
| @classmethod | ||
| def describe_outputs(cls) -> List[OutputDefinition]: | ||
| return [ | ||
| OutputDefinition(name="ocr_text", kind=[STRING_KIND]), | ||
| ] | ||
|
|
||
| @classmethod | ||
| def get_execution_engine_compatibility(cls) -> Optional[str]: | ||
| return ">=1.0.0,<2.0.0" | ||
|
|
||
|
|
||
| class StitchOCRDetectionsBlockV1(WorkflowBlock): | ||
| @classmethod | ||
| def get_manifest(cls) -> Type[WorkflowBlockManifest]: | ||
| return BlockManifest | ||
|
|
||
| def run( | ||
| self, | ||
| predictions: Batch[sv.Detections], | ||
| reading_direction: str, | ||
| tolerance: int, | ||
| ) -> BlockResult: | ||
| return [ | ||
| stitch_ocr_detections( | ||
| detections=detections, | ||
| reading_direction=reading_direction, | ||
| tolerance=tolerance, | ||
| ) | ||
| for detections in predictions | ||
| ] | ||
|
|
||
|
|
||
| def stitch_ocr_detections( | ||
| detections: sv.Detections, | ||
| reading_direction: str = "left_to_right", | ||
| tolerance: int = 10, | ||
| ) -> Dict[str, str]: | ||
| """ | ||
| Stitch OCR detections into coherent text based on spatial arrangement. | ||
| Args: | ||
| detections: Supervision Detections object containing OCR results | ||
| reading_direction: Direction to read text ("left_to_right", "right_to_left", | ||
| "vertical_top_to_bottom", "vertical_bottom_to_top") | ||
| tolerance: Vertical tolerance for grouping text into lines | ||
| Returns: | ||
| Dict containing stitched OCR text under 'ocr_text' key | ||
| """ | ||
| if len(detections) == 0: | ||
| return {"ocr_text": ""} | ||
|
|
||
| xyxy = detections.xyxy.round().astype(dtype=int) | ||
| class_names = detections.data["class_name"] | ||
|
|
||
| # Prepare coordinates based on reading direction | ||
| xyxy = prepare_coordinates(xyxy, reading_direction) | ||
|
|
||
| # Group detections into lines | ||
| boxes_by_line = group_detections_by_line(xyxy, reading_direction, tolerance) | ||
| # Sort lines based on reading direction | ||
| lines = sorted( | ||
| boxes_by_line.keys(), reverse=reading_direction in ["vertical_bottom_to_top"] | ||
| ) | ||
|
|
||
| # Build final text | ||
| ordered_class_names = [] | ||
| for i, key in enumerate(lines): | ||
| line_data = boxes_by_line[key] | ||
| line_xyxy = np.array(line_data["xyxy"]) | ||
| line_idx = np.array(line_data["idx"]) | ||
|
|
||
| # Sort detections within line | ||
| sort_idx = sort_line_detections(line_xyxy, reading_direction) | ||
|
|
||
| # Add sorted class names for this line | ||
| ordered_class_names.extend(class_names[line_idx[sort_idx]]) | ||
|
|
||
| # Add line separator if not last line | ||
| if i < len(lines) - 1: | ||
| ordered_class_names.append(get_line_separator(reading_direction)) | ||
|
|
||
| return {"ocr_text": "".join(ordered_class_names)} | ||
|
|
||
|
|
||
| def prepare_coordinates( | ||
| xyxy: np.ndarray, | ||
| reading_direction: str, | ||
| ) -> np.ndarray: | ||
| """Prepare coordinates based on reading direction.""" | ||
| if reading_direction in ["vertical_top_to_bottom", "vertical_bottom_to_top"]: | ||
| # Swap x and y coordinates: [x1,y1,x2,y2] -> [y1,x1,y2,x2] | ||
| return xyxy[:, [1, 0, 3, 2]] | ||
| return xyxy | ||
|
|
||
|
|
||
| def group_detections_by_line( | ||
| xyxy: np.ndarray, | ||
| reading_direction: str, | ||
| tolerance: int, | ||
| ) -> Dict[float, Dict[str, List]]: | ||
| """Group detections into lines based on primary coordinate.""" | ||
| # After prepare_coordinates swap, we always group by y ([:, 1]) | ||
| primary_coord = xyxy[:, 1] # This is y for horizontal, swapped x for vertical | ||
|
|
||
| # Round primary coordinate to group into lines | ||
| rounded_primary = np.round(primary_coord / tolerance) * tolerance | ||
|
|
||
| boxes_by_line = {} | ||
| # Group bounding boxes and associated indices by line | ||
| for i, (bbox, line_pos) in enumerate(zip(xyxy, rounded_primary)): | ||
| if line_pos not in boxes_by_line: | ||
| boxes_by_line[line_pos] = {"xyxy": [bbox], "idx": [i]} | ||
| else: | ||
| boxes_by_line[line_pos]["xyxy"].append(bbox) | ||
| boxes_by_line[line_pos]["idx"].append(i) | ||
|
|
||
| return boxes_by_line | ||
|
|
||
|
|
||
| def sort_line_detections( | ||
| line_xyxy: np.ndarray, | ||
| reading_direction: str, | ||
| ) -> np.ndarray: | ||
| """Sort detections within a line based on reading direction.""" | ||
| # After prepare_coordinates swap, we always sort by x ([:, 0]) | ||
| if reading_direction in ["left_to_right", "vertical_top_to_bottom"]: | ||
| return line_xyxy[:, 0].argsort() # Sort by x1 (original x or swapped y) | ||
| else: # right_to_left or vertical_bottom_to_top | ||
| return (-line_xyxy[:, 0]).argsort() # Sort by -x1 (original -x or swapped -y) | ||
|
|
||
|
|
||
| def get_line_separator(reading_direction: str) -> str: | ||
| """Get the appropriate separator based on reading direction.""" | ||
| return "\n" if reading_direction in ["left_to_right", "right_to_left"] else " " |
Oops, something went wrong.