testModules.py

# TEXT TO SPEECH

# import gtts
# from playsound import playsound
#
# tts = gtts.gTTS("Hello world")
# tts.save("hello.mp3")
# playsound("hello.mp3")
#
# # SCREEN SHARING
#
import pyautogui
# im2 = pyautogui.screenshot('my_screenshot2.png', region=(0,0,1920,1080))

# DETECTING MOUSE CLICKS

from pynput.mouse import Button, Controller
#
# mouse = Controller()

# Read pointer position
# print('The current pointer position is {0}'.format(
#     mouse.position))

# Set pointer position
# mouse.position = (10, 20)
# print('Now we have moved it to {0}'.format(
#     mouse.position))

# mouse.click(Button.left, 2)

from pynput.mouse import Listener

# def on_move(x, y):
#     print('Pointer moved to {0}'.format(
#         (x, y)))
#
#
# def on_click(x, y, button, pressed):
#     print('{0} at {1}'.format(
#         'Pressed' if pressed else 'Released',
#         (x, y)))
#     # if not pressed:
#     #     # Stop listener
#     #     return False
#
#
# def on_scroll(x, y, dx, dy):
#     print('Scrolled {0}'.format(
#         (x, y)))


# Collect events until released
# with Listener(
#         # on_move=on_move,
#         on_click=on_click) as listener:
#     # on_scroll=on_scroll)
#     listener.join()

# OPTICAL CHARACTER RECOGNITION
# import cv2
# import pytesseract
#
# img = cv2.imread('image.jpg')
#
# # Adding custom options
# custom_config = r'--oem 3 --psm 6'
# print(pytesseract.image_to_string(img, config=custom_config))

# from pynput.mouse import Listener
#
# def on_click(x, y, button, pressed):
#     print('{0} at {1}'.format(
#         'Pressed' if pressed else 'Released',
#         (x, y)))
#
#
# with Listener(on_click=on_click) as listener:
#     listener.join()

# yCoord = 0
# xCoord = 0
# counter = 0
# xLis = []
# yLis = []
# maxX = 0
# for text, top, left, width, height in zip(dict["text"], dict["top"],
#                                           dict["left"], dict["width"],
#                                           dict["height"]):
#     if text in ["The", "cat", "(Felis", "catus)"]:
#         if counter == 1:
#             yCoord = top
#             xCoord = left
#         counter += 1
#
#     if counter >= 1 and yCoord - 10 < top < yCoord + 10:
#         # img = cv2.rectangle(img, (left, top), (left + width, top + height),
#         #                     (0, 255, 0), 2)
#         xLis.append(left)
#         yLis.append(top)
#         if left == max(xLis):
#             maxX = left + width
#     elif counter >= 1 and xCoord - 10 < left < maxX:
#         # img = cv2.rectangle(img, (left, top), (left + width, top + height),
#         #                     (0, 255, 0), 2)
#         xLis.append(left)
#         yLis.append(top)
# img = cv2.rectangle(img, (min(xLis), min(yLis)), (max(xLis), max(yLis)),
#                     (255, 0, 0), 2)
# # print(pytesseract.image_to_string(img, config=custom_config))
# cv2.imshow('img', img)
# cv2.waitKey(0)

# import cv2
# import pytesseract
# from pytesseract import Output
#
# img = cv2.imread('sampleImages/fullPage2.jpg', cv2.IMREAD_GRAYSCALE)
#
# # thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
#
# custom_config = r'--oem 3 --psm 1'
# string = pytesseract.image_to_string(img, config=custom_config)
#
# sectionsLst = string.split("\n\n")
# cleanSectionLst = []
#
# # create a new list of words separated by section
# for i in sectionsLst:
#     cleanSectionLst.append(i.replace("\n", " ").split(" "))
#
# for i in range(len(cleanSectionLst)):
#     if " " in cleanSectionLst[i]:
#         cleanSectionLst[i] = list(filter(" ".__ne__, cleanSectionLst[i]))
#     if "" in cleanSectionLst[i]:
#         cleanSectionLst[i] = list(filter("".__ne__, cleanSectionLst[i]))
#
# if [] in cleanSectionLst:
#     cleanSectionLst.remove([])
#
# # extra relevant data about the words on the image - coordinates
# dataDict = pytesseract.image_to_data(img, output_type=Output.DICT,
#                                      config=custom_config)
# lstWords = list(dataDict["text"])
#
#
# def betterIdx(lst, word, num):
#     # extracts the nth duplicate's index
#     counter = 0
#     lastIdx = 0
#     for i in range(len(lst)):
#         if lst[i] == word:
#             counter += 1
#             lastIdx = i
#         if counter == num:
#             return i
#     return lastIdx
#
#
# # split each section with associated indices
# idxDict = {}
# dupWords = {}
#
# # links each section to the indices of the words in that section
# for i in cleanSectionLst:
#     val = []
#     for word in i:
#         if word in lstWords:
#             val.append(betterIdx(lstWords, word, dupWords.get(word, 1)))
#         # to avoid issues with duplicates
#         dupWords[word] = dupWords.get(word, 1) + 1
#     idxDict[" ".join(i)] = val
#
# # extracts coordinates of each word in section
# coordDict = {}
# for key in idxDict:
#     coordDict[key] = []
#     for val in idxDict[key]:
#         coordDict[key].append([dataDict["left"][val], dataDict["top"][val],
#                                dataDict["width"][val], dataDict["height"][val]])
#
# # find min and max coord of coordinates of words to get overall coordinates
# # for the whole section
#
# minMaxCoord = {}
#
# for elem in coordDict:
#     minMaxCoord[elem] = [99999, 99999, 0, 0]
#     for i in coordDict[elem]:
#         # print(i[0], i[1], minMaxCoord[elem])
#         if i[0] <= minMaxCoord[elem][0]:
#             minMaxCoord[elem][0] = i[0]
#
#         if i[0] + i[2] >= minMaxCoord[elem][2]:
#             minMaxCoord[elem][2] = i[0] + i[2]
#
#         if i[1] <= minMaxCoord[elem][1]:
#             minMaxCoord[elem][1] = i[1]
#
#         if i[1] + i[3] >= minMaxCoord[elem][3]:
#             minMaxCoord[elem][3] = i[1] + i[3]

# NEW CHANGES START HERE

# smallSections = []
#
# for text in minMaxCoord:
#     sectionSize = abs(minMaxCoord[text][2] - minMaxCoord[text][0]) * abs(minMaxCoord[text][3] - minMaxCoord[text][1])
#     if sectionSize < 20000:
#         smallSections.append(text)
#
# combinedTexts = []
# newMinMaxCoord = {}
#
#
# def combineSections(minMaxDict, text1, text2):
#     combineText = text1 + " " + text2
#     combineCoord = [min(minMaxDict[text1][0], minMaxDict[text2][0]), min(minMaxDict[text1][1], minMaxDict[text2][1]),
#                 max(minMaxDict[text1][2], minMaxDict[text2][2]), max(minMaxDict[text1][3], minMaxDict[text2][3])]
#     return combineText, combineCoord
#
#
# for text in smallSections:
#     topX = minMaxCoord[text][0]
#     topY = minMaxCoord[text][1]
#     botX = minMaxCoord[text][2]
#     botY = minMaxCoord[text][3]
#     marginY = 20
#     marginX = 40
#     if text not in combinedTexts:
#         remainList = smallSections[:smallSections.index(text)] + smallSections[smallSections.index(text) + 1:]
#         for elem in remainList:
#             if elem not in combinedTexts:
#                 if (topY - marginY < minMaxCoord[elem][3] < topY or botY < minMaxCoord[elem][1] < botY + marginY) and \
#                         (topX - marginX < minMaxCoord[elem][0] < topX or botX < minMaxCoord[elem][2] < botX + marginX):
#                     newText, newCoord = combineSections(minMaxCoord, text, elem)
#                     combinedTexts += [text, elem]
#                     newMinMaxCoord[newText] = newCoord
#
# for i in minMaxCoord:
#     if i not in smallSections or (i in smallSections and i not in combinedTexts):
#         newMinMaxCoord[i] = [minMaxCoord[i][0], minMaxCoord[i][1], minMaxCoord[i][2], minMaxCoord[i][3]]
#
# for i in newMinMaxCoord:
#     img = cv2.rectangle(img, (newMinMaxCoord[i][0], newMinMaxCoord[i][1]),
#                         (newMinMaxCoord[i][2], newMinMaxCoord[i][3]), (0, 0, 0), 2)

# for i in minMaxCoord:
#     img = cv2.rectangle(img, (minMaxCoord[i][0], minMaxCoord[i][1]),
#                         (minMaxCoord[i][2], minMaxCoord[i][3]), (0, 0, 0), 2)
# cv2.imshow('img', img)
# cv2.waitKey(0)


# check if two screenshots are the same - (abhi, StackOverFlow, 2020)
# https://stackoverflow.com/questions/1927660/compare-two-images-the-python-linux-way
# with open('sampleImages/test.jpg', 'rb') as f:
#     content1 = f.read()
# with open('sampleImages/test2.jpg', 'rb') as f:
#     content2 = f.read()
# if content1 == content2:
#     print("same")
# else:
#     print("not same")

# from pynput import keyboard
#
#
# def on_press(key):
#     print(key)
#
#
# with keyboard.Listener(
#         on_press=on_press) as listener:
#     listener.join()

# import cv2
# import pytesseract
# from pytesseract import Output
#
# img = cv2.imread('sampleImages/ArPageTest.jpg', cv2.IMREAD_GRAYSCALE)
#
# custom_config = r'--oem 3 --psm 1'
# string = pytesseract.image_to_string(img, config=custom_config, lang="ara")
# dataDict = pytesseract.image_to_data(img, output_type=Output.DICT, config=custom_config, lang="ara")
# print(dataDict["text"])

# import cv2
# import pytesseract
# from pytesseract import Output
#
# img = cv2.imread('sampleImages/ArPageTest.jpg', cv2.IMREAD_GRAYSCALE)
#
# custom_config = r'--oem 3 --psm 1'
# string = pytesseract.image_to_string(img, config=custom_config, lang="ara")
#
# sectionsLst = string.split("\n\n")
# cleanSectionLst = []
#
# # create a new list of words separated by section
# for i in sectionsLst:
#     cleanSectionLst.append(i.replace("\n", " ").split(" "))
#
# for i in range(len(cleanSectionLst)):
#     if " " in cleanSectionLst[i]:
#         cleanSectionLst[i] = list(filter(" ".__ne__, cleanSectionLst[i]))
#     if "" in cleanSectionLst[i]:
#         cleanSectionLst[i] = list(filter("".__ne__, cleanSectionLst[i]))
#
# if [] in cleanSectionLst:
#     cleanSectionLst.remove([])
#
# # extra relevant data about the words on the image - coordinates
# dataDict = pytesseract.image_to_data(img, output_type=Output.DICT, config=custom_config, lang="ara")
#
# lstWords = list(dataDict["text"])
#
#
# def betterIdx(lst, word, num):
#     # extracts the nth duplicate's index
#     counter = 0
#     lastIdx = 0
#     for i in range(len(lst)):
#         if lst[i] == word:
#             counter += 1
#             lastIdx = i
#         if counter == num:
#             return i
#     return lastIdx
#
#
# # split each section with associated indices
# idxDict = {}
# dupWords = {}
#
# # links each section to the indices of the words in that section
# for i in cleanSectionLst:
#     val = []
#     for word in i:
#         if word in lstWords:
#             val.append(betterIdx(lstWords, word, dupWords.get(word, 1)))
#         # to avoid issues with duplicates
#         dupWords[word] = dupWords.get(word, 1) + 1
#     idxDict[" ".join(i)] = val
#
# # extracts coordinates of each word in section
# coordDict = {}
# for key in idxDict:
#     coordDict[key] = []
#     for val in idxDict[key]:
#         coordDict[key].append([dataDict["left"][val], dataDict["top"][val],
#                                dataDict["width"][val], dataDict["height"][val]])
#
# # find min and max coord of coordinates of words to get overall coordinates
# # for the whole section
#
# minMaxCoord = {}
#
# for elem in coordDict:
#     minMaxCoord[elem] = [99999, 99999, 0, 0]
#     for i in coordDict[elem]:
#         # print(i[0], i[1], minMaxCoord[elem])
#         if i[0] <= minMaxCoord[elem][0]:
#             minMaxCoord[elem][0] = i[0]
#
#         if i[0] + i[2] >= minMaxCoord[elem][2]:
#             minMaxCoord[elem][2] = i[0] + i[2]
#
#         if i[1] <= minMaxCoord[elem][1]:
#             minMaxCoord[elem][1] = i[1]
#
#         if i[1] + i[3] >= minMaxCoord[elem][3]:
#             minMaxCoord[elem][3] = i[1] + i[3]
#
#
# for i in minMaxCoord:
#     img = cv2.rectangle(img, (minMaxCoord[i][0], minMaxCoord[i][1]),
#                         (minMaxCoord[i][2], minMaxCoord[i][3]), (0, 0, 0), 2)
#
# cv2.imshow('img', img)
# cv2.waitKey(0)

import gtts
from playsound import playsound
import os
#
# tts = gtts.gTTS("My name is Iman and I love chocolate ", lang="en", slow=True)
# tts.save("instructionsArSpeech.mp3")
# playsound("instructionsArSpeech.mp3")
# os.remove("instructionsArSpeech.mp3")


from pynput import keyboard
#
#
# def on_press(key):
#     if key.char == "a":
#         print(key.char)
#     try:
#         if key.char == 'd':
#             print("hi", key.char)
#     except AttributeError:
#         if key == 'd':
#             print("bye", key.char)


#
# with keyboard.Listener(
#         on_press=on_press) as listener:
#     listener.join()

import tkinter as tk
import tkinter.font as tkFont

# gui = tk.Tk()
#
# gui.geometry("500x500")
# gui.title("Screen Reader for Visually Impaired")
#
# frame = tk.Frame(gui)
# frame.pack(side="top", expand=True, fill="both")
# instruction = """This application is a screen reader that helps you
#                     navigate websites. In order to use this screen reader
#                     go to the webpage that you want to be read and press alt.
#                     When you press alt your mouse will be moved to the bottom left
#                     of the page. In order to stop the program press Escape.
#                     You can now begin to use the screen reader. To change the dialect press D. In order to
#                     repeat the instructions in English press the spacebar. To repeat the instructions in arabic press A.
#                     To leave the instructions press L."""
# fontStyle = tkFont.Font(family="Lucida Grande", size=12)
# instructionLabel = tk.Label(frame,padx = 10, compound = tk.CENTER, text=instruction, font=fontStyle).pack(side="left")
# # instructionLabel.place()
# gui.mainloop()

# import tkinter as tk
#
# gui = tk.Tk()
# gui.geometry("500x500")
# gui.title("Screen Reader for Visually Impaired")
# instruction = """This application is a screen reader that helps you navigate websites.
# In order to use this screen reader go to the webpage that you want to be read
# and press alt. When you press alt your mouse will be moved to the
# bottom left of the page. In order to stop the program press
# Escape. You can now begin to use the screen reader.
# To change the dialect press D. In order to repeat the
# instructions in English press the spacebar. To repeat the
# instructions in arabic press A. To leave the instructions press L."""
#
# instrucLabel = tk.Label(gui, compound=tk.RIGHT, text=instruction, pady=50).pack(anchor="center")
#
# gui.mainloop()

# from pynput.mouse import Button, Controller
#
# mouse = Controller()
# mouseLoc = list(mouse.position)
# print(mouseLoc)
import gtts
from playsound import playsound
import pyautogui
from pynput import keyboard
from pynput import mouse
from pynput.mouse import Controller
import cv2
import pytesseract
from cmu_112_graphics import *
from pytesseract import Output

# urlImg = pyautogui.screenshot('fullPgScreenshot.png', region=(100, 50, 1000, 51))
#
# pngToJpg = cv2.imread('fullPgScreenshot.png')
#
# invert = cv2.bitwise_not(pngToJpg)
#
# cv2.imwrite('fullPgScreenshot.jpg', invert)
#
# # custom_config = r'--oem 3 --psm 1'
# string = pytesseract.image_to_string(invert)
# os.remove('fullPgScreenshot.png')
# # os.remove('fullPgScreenshot.jpg')
# print(string)
# for i in string.split(" "):
#     if "/" in i or ".com" in i or ".org" in i:
#         print(i)

# urlImg = pyautogui.screenshot('fullPgScreenshot.png', region=(0, 870, 720, 900))
# pngToJpg = cv2.imread('fullPgScreenshot.png')
# invert = cv2.bitwise_not(pngToJpg)
# cv2.imwrite('fullPgScreenshot.jpg', invert)
# os.remove('fullPgScreenshot.png')
# string = pytesseract.image_to_string(invert)
# print(string)

from imageai.Detection import ObjectDetection

# BUG FIX - for tensor flow error
# (GTS, StackOverFlow, 2021)
# https://stackoverflow.com/questions/66092421/how-to-rebuild-tensorflow-with-the-compiler-flags
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# Using a pretrained model to detect the location of an image

# (Priyal, Medium, 2020)
# https://medium.datadriveninvestor.com/ai-object-detection-using-only-10-lines-of-code-imageai-89d3ba9886ea
#
# DOWNLOAD PREDEFINED MODEL FROM HERE - https://github.com/priyalwalpita/ai_object_detection

# execution_path = os.getcwd()
# detector = ObjectDetection()
# detector.setModelTypeAsRetinaNet()
# detector.setModelPath(os.path.join(execution_path, "resnet50_coco_best_v2.1.0.h5"))
# detector.loadModel()
# detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path, "sampleImages/imgTest.jpg"),
#                                              output_image_path=os.path.join(execution_path, "imagenew.jpg"))
#
# # determine all image locations and what objects are in the image
# imgLocs = {}
# for (detect, idx) in zip(detections, range(len(detections))):
#     # deals with the same object found in different locations
#     if detect['name'] in imgLocs:
#         imgLocs[detect['name'] + str(idx)] = detect['box_points']
#     else:
#         imgLocs[detect['name']] = detect['box_points']
#
#
# # to avoid repetition of already combined objects
#
# combinedImgsCheck = []
# combinedImgsNestLst = []
#
# # find images that contain more than one object, based on distance or overlap of bounding box
#
# for (obj1, idx) in zip(imgLocs, range(len(imgLocs))):
#
#     similarCoord = [obj1]
#
#     for (obj2, idx2) in zip(imgLocs, range(len(imgLocs))):
#
#         if obj2 not in similarCoord and obj2 not in combinedImgsCheck:
#
#             # extract coordinates from dictionary
#
#             [img1XStart, img1YStart, img1XEnd, img1YEnd] = imgLocs[obj1]
#             [img2XStart, img2YStart, img2XEnd, img2YEnd] = imgLocs[obj2]
#
#             # 2 SCENARIOS - check if bounding boxes of objects overlap or are close to one another
#
#             # 1 - overlap
#
#             if ((img1XStart <= img2XStart <= img1XEnd) or (img1XStart <= img2XEnd <= img1XEnd)) and \
#                     (img1YStart <= img2YStart <= img1YEnd) or (img1YStart <= img2YEnd <= img1YEnd):
#                 similarCoord.append(obj2)
#
#             # 2 - close to each other
#
#             elif ((img1XEnd <= img2XStart <= img1XEnd + 10) or (img1XStart - 10 <= img2XEnd <= img1XStart)) and \
#                     ((img1YEnd <= img2YStart <= img1YEnd + 10) or (img1YStart - 10 <= img2YEnd <= img1YStart)):
#                 similarCoord.append(obj2)
#
#     # combine into a list of combined images
#
#     if len(similarCoord) > 1:
#         combinedImgsNestLst.append(similarCoord)
#         combinedImgsCheck += similarCoord
#
# #  if multiple objects are found in an image; combine their locations and text into a user-friendly manner
#
# newImgLocs = {}
# for imgName in imgLocs:
#     if imgName not in combinedImgsCheck:
#         newImgLocs[imgName] = imgLocs[imgName]
#     else:
#         for combinedImgLst in combinedImgsNestLst:
#             combinedName = " ".join(combinedImgLst)
#             allCoord = [[], [], [], []]
#
#             # combine the coordinates of all objects that overlap or close to each other to make one bigger object
#             for combinedImgName in combinedImgLst:
#                 allCoord[0].append(imgLocs[combinedImgName][0])
#                 allCoord[1].append(imgLocs[combinedImgName][1])
#                 allCoord[2].append(imgLocs[combinedImgName][2])
#                 allCoord[3].append(imgLocs[combinedImgName][3])
#
#             # combine the coordinates
#             combinedCoord = [min(allCoord[0]), min(allCoord[1]), max(allCoord[2]), max(allCoord[3])]
#
#             newImgLocs[combinedName] = combinedCoord
#
# # change text to be user friendly with use in text to speech
# finalImgLocs = {}
#
# for name in newImgLocs:
#     nameLst = name.split(" ")
#     if len(nameLst) > 1:
#         firstPtLst = " ".join(nameLst[:-1])
#         secondPtLst = nameLst[-1]
#         finalImgLocs[f"You are hovering over an image that contains {firstPtLst} and {secondPtLst}"] = newImgLocs[name]
#     else:
#         finalImgLocs[f"You are hovering over an image that contains {nameLst[0]}"] = newImgLocs[name]
#
# print(finalImgLocs)

# img = cv2.imread('sampleImages/imgTest.jpg')
#
# for i in newImgLocs:
#     img = cv2.rectangle(img, (newImgLocs[i][0], newImgLocs[i][1]),
#                         (newImgLocs[i][2], newImgLocs[i][3]), (0, 0, 0), 2)
#
# cv2.imshow('img', img)
# cv2.waitKey(0)

from googletrans import Translator, constants
translator = Translator()
translation = translator.translate("Hello", dest="ar")
print(translation.text)