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TrainingDataGenerationCOCO/CocoPanopticToSemanticMap.py

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+
+#Generate training data for the net using the COCO panoptic 2017 dataset take classes related to vessels and create binary maps of all thesel classes
+import numpy as np
+import os
+#import scipy.misc as misc
+import random
+import cv2
+import json
+import threading
+import scipy.misc as misc
+#######################################Convert RGB image to label #####################################################################
+def rgb2id(color): # Convert annotation map from 3 channel RGB to instance
+    if isinstance(color, np.ndarray) and len(color.shape) == 3:
+        if color.dtype == np.uint8:
+            color = color.astype(np.uint32)
+        return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
+    return color[0] + 256 * color[1] + 256 * 256 * color[2]
+#########################################################################################################################
+######################################Generate vessels mask from instance segments of the COCO set by merging all instance corres###################################################################################
+class Generator:
+# Initiate reader and define the main parameters for the data reader
+    def __init__(self, ImageDir,AnnotationDir,OutDir, DataFile, AnnotationFileType="png", ImageFileType="jpg",UnlabeledTag=0,VesselCats=[44,46,47,51,86],IgnoreCats=[70,81,81,50]):
+
+        self.ImageDir=ImageDir # Image dir
+        self.AnnotationDir=AnnotationDir # File containing image annotation
+        self.AnnotationFileType=AnnotationFileType # What is the the type (ending) of the annotation files
+        self.ImageFileType=ImageFileType # What is the the type (ending) of the image files
+        self.DataFile=DataFile # Json File that contain data on the annotation of each image
+        self.UnlabeledTag=UnlabeledTag # Value of unlabled region in the annotation map (usually 0)
+
+        self.Outdir=OutDir
+        self.SemanticDir = OutDir + "/SemanticMaps/"
+        self.OutImageDir = OutDir + "/Image/"
+        # self.MinSegSize = 0#400
+        if not os.path.exists(OutDir): os.mkdir(OutDir)
+        if not os.path.exists(self.SemanticDir): os.mkdir(self.SemanticDir)
+        # if not os.path.exists(self.SemanticSpecialDir): os.mkdir(self.SemanticSpecialDir)
+        #
+        if not os.path.exists(self.OutImageDir): os.mkdir(self.OutImageDir)
+
+
+        self.VesselCats=VesselCats #class of vessel like object [44] 'bottle',[46] 'wine glass',[47] = 'cup',[51] = 'bowl', [86] = 'vase'
+        self.IgnoreCats=IgnoreCats#ambigious classes that should be ignored[70,81,50,64,196] # Unclear wether they vessel or not to ignore #70 toilet 81 sink 50 spoon64,196] # Unclear wether they vessel or not to ignore #70 toilet 81 sink 50 spoon, 196,food-other-merged 64,potted plant
+        #) bottle]
+      #  self.PickBySize = False  # Pick instances of with probablity proportional to their sizes if false all segments are picked with equal probablity
+#........................Read data file................................................................................................................
+        with open(DataFile) as json_file:
+            self.AnnData=json.load(json_file)
+
+#-------------------Get All files in folder--------------------------------------------------------------------------------------
+        self.FileList=[]
+        for FileName in os.listdir(AnnotationDir):
+            if AnnotationFileType in FileName:
+                self.FileList.append(FileName)
+
+##############################################################################################################################################
+#Get annotation data for specific image from the json file
+    def GetAnnnotationData(self, AnnFileName):
+            for item in self.AnnData['annotations']:  # Get Annotation Data
+                if (item["file_name"] == AnnFileName):
+                    return(item['segments_info'])
+# ############################################################################################################################################
+# #Get information for specific catagory/Class id
+    def GetCategoryData(self,ID):
+                for item in self.AnnData['categories']:
+                    if item["id"]==ID:
+                        return item["name"],item["isthing"]
+                return "", 0
+
+######################################################################################################
+#Generate list of all  segments in the image also generate semantic map
+# Given the annotation map a json data file create list of all segments and instance with info on each segment and merge all the segment
+#--------------------------Generate list of all segments--------------------------------------------------------------------------------
+    def GenerateSemanticMap(self,Ann,Ann_name):
+        AnnList = self.GetAnnnotationData(Ann_name)
+        h,w=Ann.shape
+        SemanticMap = np.zeros([h,w],dtype=np.uint8) # 1 vessel cat, 0 other cat
+        ROIMap = np.zeros([h, w], dtype=np.uint8) #  1 Annoted region, 0 not annotated or not clear
+        for an in AnnList:
+            ct=an["category_id"]
+            if ct in  self.VesselCats:
+                            SemanticMap[Ann == an['id']] = 1
+            if not (ct in  self.IgnoreCats):
+                            ROIMap[Ann == an['id']] = 1
+        return SemanticMap, ROIMap
+
+
+
+####################################################Go over all files and convert annotation to vessel mask##############################################################################################
+    def Generate(self):
+
+           # for ID in range(200): # Display all cata
+           #     name,Isthing=self.GetCategoryData(ID)
+           #     print(str(ID)+") "+name)
+           # ErrorCount=0
+
+           for f,Ann_name in enumerate(self.FileList): # Get label image name
+                print(str(f) + ")" + Ann_name)
+                Ann = cv2.imread(self.AnnotationDir + "/" + Ann_name)  # Load Annotation
+                Ann = Ann[..., :: -1]
+                self.AnnColor = Ann
+                Ann = rgb2id(Ann)
+                H, W = Ann.shape
+                SemanticMap, ROIMask = self.GenerateSemanticMap(Ann, Ann_name)  # Generate list of all segments in image
+
+
+#                if os.path.exists(self.SegMapDir + "/" + Ann_name): continue
+                print(str(f)+")"+Ann_name)
+
+                Ann = cv2.imread(self.AnnotationDir + "/" + Ann_name)  # Load Annotation
+
+                if not (1 in SemanticMap):continue
+                from shutil import copyfile
+
+                copyfile(self.ImageDir + "/" + Ann_name.replace(".png",".jpg"), self.OutImageDir+ "/" + Ann_name.replace(".png",".jpg"))
+#-------------------------------------------Save semanic map---------------------------------------------------------------------------------------------------
+
+                SemanticMap=np.expand_dims(SemanticMap,axis=2)
+                ROIMask=np.expand_dims(ROIMask,axis=2)
+
+                SemanticMap= np.concatenate([SemanticMap, ROIMask,SemanticMap],axis=2)
+                cv2.imwrite(self.SemanticDir + "/" + Ann_name, SemanticMap.astype(np.uint8))
+
+
+
+
+
+

TrainingDataGenerationCOCO/RunDataGeneration.py

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+#Generate training data for vessel semantic segmentation, using related class from the COCO panoptic dataset
+#http://cocodataset.org/#download # Download Train images and train annotation for the pnoptic set and set path in the input dirs variables
+
+
+import CocoPanopticToSemanticMap as Generator
+############################################Input and ouput dir location################################################################################################################################################
+ImageDir="/scratch/gobi1/seppel/DataSets/COCO_PANOPTIC/PanopticFull/train2017/" # image folder (coco training) train set
+AnnotationDir="/scratch/gobi1/seppel/DataSets/COCO_PANOPTIC/PanopticFull/panoptic_train2017/" # annotation maps folder from coco panoptic train set
+DataFile="/scratch/gobi1/seppel/DataSets/COCO_PANOPTIC/PanopticFull/panoptic_train2017.json" # Json Data file coco panoptic train set
+
+
+OutDir="/scratch/gobi2/seppel/Chemscape/FromOtherDataBase/COCO_Vessel_Related_Class/" # Output Dir
+###############################Vessel cats in COCO###################################################################################################################
+
+
+VesselCats = [44,46,47,51,86] # Cats to use as vessel #[44] 'bottle',[46] 'wine glass',[47] = 'cup',[51] = 'bowl', [86] = 'vase'
+IgnoreCats = [70,50,64,196] # Unclear wether they are vessel or not.  To ignore #70 toilet 81 sink 50 spoon, 196,food-other-merged 64,potted plant
+##########################################################################################################################################################
+x=Generator.Generator(ImageDir,AnnotationDir,OutDir, DataFile, VesselCats=VesselCats,IgnoreCats=IgnoreCats) # Create class
+x.Generate() # Run conversion