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imgDetection.py
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imgDetection.py
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import cv2
from PIL import ImageFont, ImageDraw, Image
""" Converts user provided string to an images that will be used to find the
sensitive data from a target file.
"""
def privateDataImg(privateString):
# Selects font for the user provided string (Using Ubuntu's font path)
font = ImageFont.truetype(
"/usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf", 14, encoding="unic")
# Dimentions of the font
fontWidth, fontHeight = font.getsize(privateString)
# Creates the background of the picture for the user string
privateImgField = Image.new(
"RGB", (fontWidth + 10, fontHeight + 10), "white")
# Draws the string onto the created white rectangle
writePrivateImgField = ImageDraw.Draw(privateImgField)
writePrivateImgField.text((5, 5), privateString, "black", font)
# Saves the final file
privateImgField.save("privateData.png", "PNG")
"""Perfoms template matching function """
def imgTemplateMatch(templetaImageName, imageToScanName):
# Loads the string image and target images in gray scale
imageScan = cv2.imread(imageToScanName, 0)
templateImage = cv2.imread(templetaImageName, 0)
# Stores the dimentions of the template
templateWidth, templateHight = templateImage.shape[::-1]
# Use template matching function with method TM_CCOEFF_NORMED
result = cv2.matchTemplate(imageScan, templateImage, cv2.TM_CCOEFF_NORMED)
# Calculate the rectangle dimentions around the detected area
minumum, maximum, minLocation, maxLocation = cv2.minMaxLoc(result)
lowCornerFormula = (
maxLocation[0] + templateWidth, maxLocation[1] + templateHight)
copyimageScan = imageScan.copy()
# Draws rectangle
cv2.rectangle(copyimageScan, maxLocation, lowCornerFormula, 255, -1)
cv2.imwrite("output.png", copyimageScan)
# cv2.imshow('image',copyimageScan)
# cv2.waitKey(0)
""" Utilizes machine learing (cascade file) file to detect objects. Faces cascade will be
used by default. """
def objectDetection(filename):
faceCascade = cv2.CascadeClassifier(
"./resources/haarcascade_frontalface_alt.xml")
img = cv2.imread(filename)
tempGrayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Utilizes detectMultiScale to find the objects
faces = faceCascade.detectMultiScale(tempGrayImg, 1.3, 5)
# Calculates the rectangle based on two points
for (xDir, yDir, width, height) in faces:
cv2.rectangle(img, (xDir, yDir), (xDir + width,
yDir + height), (255, 0, 0), -1)
cv2.imwrite("finalOutput.png", img)
# cv2.imshow("Faces", img)
# cv2.waitKey(0)
def RUN(userInput, filename):
privateDataImg(userInput)
imgTemplateMatch("privateData.png", filename)
objectDetection("output.png")