''' MIT license https://opensource.org/licenses/MIT Copyright 2024 Infosys Ltd Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' import cv2 from PIL import Image, ImageChops from presidio_image_redactor import ImageRedactorEngine from presidio_image_redactor.image_analyzer_engine import ImageAnalyzerEngine import matplotlib import io from numpy import asarray from matplotlib import pyplot as plt import string import random from typing import Optional import time from presidio_anonymizer import AnonymizerEngine from presidio_image_redactor import ImageAnalyzerEngine, BboxProcessor from PIL import Image, ImageDraw, ImageChops from presidio_anonymizer.entities import (RecognizerResult, OperatorResult, OperatorConfig) from typing import Union, Tuple, Optional def fig2img(fig): """Convert a Matplotlib figure to a PIL Image and return it.""" buf = io.BytesIO() fig.savefig(buf) buf.seek(0) img = Image.open(buf) return img class Detect: def getFace(image:Image): # print(type(image)) image= asarray(image) # print(image) face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # print(len(image.shape)) # print(image.shape) if(len(image.shape)==3): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) elif(len(image.shape)==2): gray = image else: gray = image # print(gray) # Detect faces in the image faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) # print("done") # print(faces) # Extract and save each detected face for i, (x, y, w, h) in enumerate(faces): face = image[y:y+h, x:x+w] # print(x,y,w,h) # cv2.imwrite(f'C:\\WORK\\GIT\\responsible-ai-privacy\\responsible-ai-privacy\\src\\face_{i}.jpg', face) # print("returning") return(x,y,w,h) class EncryptImage: text="" entity=[] def __init__(self, image_analyzer_engine: Optional[ImageAnalyzerEngine] = None): if not image_analyzer_engine: image_analyzer_engine = ImageAnalyzerEngine() self.image_analyzer_engine = image_analyzer_engine self.bbox_processor = BboxProcessor() # redactorEngine = ImageRedactorEngine(image_analyzer_engine=self.image_analyzer_engine) def getText(self,image:Image,ocr_kwargs:Optional[dict] = None,**analyzer_data): # "Code/Functions from image_analyzer_engine" print(type(image)) perform_ocr_kwargs, ocr_threshold = self.image_analyzer_engine._parse_ocr_kwargs(ocr_kwargs) ocr_result = self.image_analyzer_engine.ocr.perform_ocr(image, **perform_ocr_kwargs) # Apply OCR confidence threshold if it is passed in if ocr_threshold: ocr_result = self.image_analyzer_engine.threshold_ocr_result(ocr_result, ocr_threshold) # Analyze text text = self.image_analyzer_engine.ocr.get_text_from_ocr_dict(ocr_result) EncryptImage.text=text def imageAnonimyze( self, image: Image, fill: Union[int, Tuple[int, int, int]] = (0, 0, 0), ocr_kwargs: Optional[dict] = None, encryptionList:Optional[list]=[], **text_analyzer_kwargs,): """"Code/Functions from Image Redactor""" image = ImageChops.duplicate(image) bboxes = self.image_analyzer_engine.analyze( image, ocr_kwargs, **text_analyzer_kwargs ) # EncryptImage.entity.extend(bboxes) print("box==========",bboxes) draw = ImageDraw.Draw(image) for box in bboxes: if(box.entity_type in encryptionList): EncryptImage.entity.append(box) x0 = box.left y0 = box.top x1 = x0 + box.width y1 = y0 + box.height # print("=================",x0,y0) draw.rectangle([x0, y0, x1, y1], fill=fill) if("entities" in text_analyzer_kwargs): if("Face_Detect" in text_analyzer_kwargs["entities"]): res=Detect.getFace(image) if(res==None): pass elif(len(res)==4): x,y,w,h=res draw.rectangle([x-(x*.05),y-(x*.05),x+w+(w*.25),y+h+(h*.25)], fill=fill) # x0,y0,x1,y1=(738, 1028, 217, 58) # draw.rectangle([x-(x*.05),y-(x*.05),x+w+(w*.25),y+h+(h*.25)], fill=fill) return image def dis(): print("===========",EncryptImage.text) print("---------",EncryptImage.text.title()) # print("==========",EncryptImage.entity) print("--------------------") def encrypt(self,image:Image,ocr_kwargs:Optional[dict] = None,encryptionList=[],**analyzer_data): """"Code Reference from Image Verify""" image = ImageChops.duplicate(image) image_x, image_y = image.size bboxes=EncryptImage.entity text=EncryptImage.text # print("boxA==",bboxes) dict_operators={} if encryptionList is not None and len(encryptionList) >0 : for entity in encryptionList: dict_operators.update({entity: OperatorConfig("hash", {"hash-type": 'md5'})}) else: dict_operators = None # print("dic=",dict_operators) analyzer_result=bboxes # print(analyzer_result) anonymizeEngine=AnonymizerEngine() anonymizeResult=anonymizeEngine.anonymize(text=text, operators=dict_operators, analyzer_results=analyzer_result) # print(r.items) # print("==========",anonymizeResult.items) result=anonymizeResult.items result=sorted(result, key=lambda i: i.start) # print("RRRRRRRRR=========",len(result),len(bboxes)) fig, ax = plt.subplots() ax.axis('off') image_r = 70 fig.set_size_inches(image_x / image_r, image_y / image_r) res=[] drawen=[] excess=0 if len(bboxes) == 0: return (image,res) else: for i in range(len(bboxes)): box=bboxes[i] N=box.end-box.start if((box.start,box.end)in drawen): excess+=1 continue # print("i====######",i) drawen.append((box.start,box.end)) if((i-excess)