[SPARKNLP-1131] - Introducing Florance-2 (#14585)

* Florence2 Scala API

* preprocessing and postprocessing for images

* test images

* python API and tests

* notebooks and docs

* changed postprocessing and added tests for all the tasks

Author: prabod

docs/en/transformer_entries/Florence2Transformer.md

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+{%- capture title -%}
+Florence2Transformer
+{%- endcapture -%}
+
+{%- capture description -%}
+Florence2Transformer can load Florence-2 models for a wide variety of vision and vision-language tasks using prompt-based inference.
+
+Florence-2 is an advanced vision foundation model from Microsoft that uses a prompt-based approach to handle tasks like image captioning, object detection, segmentation, OCR, and more. The model leverages the FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. Its sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+
+```scala
+val florence2 = Florence2Transformer.pretrained()
+     .setInputCols("image_assembler")
+     .setOutputCol("answer")
+```
+The default model is `"florence2_base_ft_int4"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Vision+Tasks).
+
+==Supported Tasks==
+
+Florence-2 supports a variety of tasks through prompt engineering. The following prompt tokens can be used:
+
+- <CAPTION>: Image captioning
+- <DETAILED_CAPTION>: Detailed image captioning
+- <MORE_DETAILED_CAPTION>: Paragraph-level captioning
+- <CAPTION_TO_PHRASE_GROUNDING>: Phrase grounding from caption (requires additional text input)
+- <OD>: Object detection
+- <DENSE_REGION_CAPTION>: Dense region captioning
+- <REGION_PROPOSAL>: Region proposal
+- <OCR>: Optical Character Recognition (plain text extraction)
+- <OCR_WITH_REGION>: OCR with region information
+- <REFERRING_EXPRESSION_SEGMENTATION>: Segmentation for a referred phrase (requires additional text input)
+- <REGION_TO_SEGMENTATION>: Polygon mask for a region (requires additional text input)
+- <OPEN_VOCABULARY_DETECTION>: Open vocabulary detection for a phrase (requires additional text input)
+- <REGION_TO_CATEGORY>: Category of a region (requires additional text input)
+- <REGION_TO_DESCRIPTION>: Description of a region (requires additional text input)
+- <REGION_TO_OCR>: OCR for a region (requires additional text input)
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+IMAGE
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture python_example -%}
+import sparknlp
+from sparknlp.base import *
+from sparknlp.annotator import *
+from pyspark.ml import Pipeline
+from pyspark.sql.functions import lit
+
+image_df = spark.read.format("image").load(path=images_path) # Replace with your image path
+test_df = image_df.withColumn("text", lit("<OD>"))
+
+imageAssembler = ImageAssembler()   
+          .setInputCol("image")   
+          .setOutputCol("image_assembler")
+
+florence2 = Florence2Transformer.pretrained()   
+          .setInputCols(["image_assembler"])   
+          .setOutputCol("answer")
+
+pipeline = Pipeline().setStages([
+          imageAssembler,
+          florence2
+])
+
+result = pipeline.fit(test_df).transform(test_df)
+result.select("image_assembler.origin", "answer.result").show(False)
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import spark.implicits._
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions.lit
+
+val imageFolder = "path/to/your/images" // Replace with your image path
+
+val imageDF: DataFrame = spark.read
+     .format("image")
+     .option("dropInvalid", value = true)
+     .load(imageFolder)
+
+val testDF: DataFrame = imageDF.withColumn("text", lit("<OD>"))
+
+val imageAssembler: ImageAssembler = new ImageAssembler()
+     .setInputCol("image")
+     .setOutputCol("image_assembler")
+
+val florence2 = Florence2Transformer.pretrained()
+     .setInputCols("image_assembler")
+     .setOutputCol("answer")
+
+val pipeline = new Pipeline().setStages(Array(
+     imageAssembler,
+     florence2
+))
+
+val result = pipeline.fit(testDF).transform(testDF)
+
+result.select("image_assembler.origin", "answer.result").show(false)
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[Florence2Transformer](/api/com/johnsnowlabs/nlp/annotators/cv/Florence2Transformer)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[Florence2Transformer](/api/python/reference/autosummary/sparknlp/annotator/cv/florence2_transformer/index.html#sparknlp.annotator.cv.florence2_transformer.Florence2Transformer)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[Florence2Transformer](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/Florence2Transformer.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+   title=title
+   description=description
+   input_anno=input_anno
+   output_anno=output_anno
+   python_example=python_example
+   scala_example=scala_example
+   api_link=api_link
+   python_api_link=python_api_link
+   source_link=source_link
+%}