If i import pyttsx3 library my camera is not opening of opencv2. However no error is showing up

Question

This is my project: The Visual Impairment Assistance uses object-detection algorithms & TensorFlow APIs for real-time support. Operating through a laptop-based wireless network, it provides voice-based feedback, conveying concise information about objects and obstacles. This user-friendly system enhances independence & safety for individuals with visual impairment. Down below is the problem I am facing: When I am running the code without pyttsx3 library the camera is opening and the code working fine. But the moment I import pyttsx3 library and initialise the engine the code runs once and camera does not get opened.

THIS IS THE CODE:

import numpy as np
import os
import six.moves.urllib as urllib
import urllib.request as allib
import sys
import tarfile
import tensorflow as tf
import zipfile
import time
import pytesseract
import engineio 
from gtts import gTTS
import playsound
import urllib.request
import torch
from torch.autograd import Variable as V
# import models as models
from torchvision import transforms as trn
from torch.nn import functional as F
import objc

import pyttsx3
# # from .engine import Engine
engine =pyttsx3.init()


from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

arch = 'resnet18'

model_file = 'whole_%s_places365_python36.pth.tar' % arch
if not os.access(model_file, os.W_OK):
    weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
    os.system('wget ' + weight_url)


#= label_map_util.create_category_index(categories)



import label_map_util
#/object_detection/'   m2

import visualization_utils as vis_util
PATH_TO_CKPT = 'models/research/object_detection/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb'
PATH_TO_LABELS = 'models/research/object_detection/mscoco_label_map.pbtxt'
NUM_CLASSES = 90

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.compat.v1.GraphDef()
  with tf.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

url='http://10.67.208.240:8080//shot.jpg'

import cv2
cap = cv2.VideoCapture(0)

with detection_graph.as_default():
  with tf.compat.v1.Session(graph=detection_graph) as sess:
   ret = True
   while (ret):
      ret,image_np = cap.read()
      
      if cv2.waitKey(20) & 0xFF == ord('b'): 
       
          cv2.imwrite('opencv'+'.jpg', image_np) 
      
    
    
          model_file = 'whole_%s_places365_python36.pth.tar' % arch
          if not os.access(model_file, os.W_OK):
              weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
              os.system('wget ' + weight_url)
        
          useGPU = 1
          if useGPU == 1:
              model = torch.load(model_file)
          else:
              model = torch.load(model_file, map_location=lambda storage, loc: storage) # model trained in GPU could be deployed in CPU machine like this!
        
   
          model.eval()
       
          centre_crop = trn.Compose([
                trn.Resize((256,256)),
                trn.CenterCrop(224),
                trn.ToTensor(),
                trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
          ])
    
        
     
      
          file_name = 'categories_places365.txt'
          if not os.access(file_name, os.W_OK):
              synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_places365.txt'
              os.system('wget ' + synset_url)
          classes = list()
          with open(file_name) as class_file:
              for line in class_file:
                  classes.append(line.strip().split(' ')[0][3:])
          classes = tuple(classes)
    
    
        
          img_name = 'opencv.jpg'
          if not os.access(img_name, os.W_OK):
              img_url = 'http://places.csail.mit.edu/demo/' + img_name
              os.system('wget ' + img_url)
    
          img = Image.open(img_name)
          input_img = V(centre_crop(img).unsqueeze(0), volatile=True)
        

          logit = model.forward(input_img)
          h_x = F.softmax(logit, 1).data.squeeze()
          probs, idx = h_x.sort(0, True)
        
          print('POSSIBLE SCENES ARE: ' + img_name)
          engine.say("Possible Scene may be")
          engine.say(img_name)
          
        
          for i in range(0, 5):
              engine.say(classes[idx[i]])
              print('{}'.format(classes[idx[i]]))
      
       
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      # Each box represents a part of the image where a particular object was detected.
      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      # Each score represent how level of confidence for each of the objects.
      # Score is shown on the result image, together with the class label.
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      # Actual detection.
      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      
      
      
      
      
      # Visualization of the results of a detection.
      if cv2.waitKey(2) & 0xFF == ord('a'):
          vis_util.vislize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      else:    
          vis_util.visualize_boxes_and_labels_on_image_array(
              image_np,
              np.squeeze(boxes),
              np.squeeze(classes).astype(np.int32),
              np.squeeze(scores),
              category_index,
              use_normalized_coordinates=True,
              line_thickness=8)
      if cv2.waitKey(2) & 0xFF == ord('r'):
          text=pytesseract.image_to_string(image_np)
          print(text)
          engine.say(text)
          engine.runAndWait()
      
    
            
      for i,b in enumerate(boxes[0]):
      
          
        #                 car                    bus                  truck
        if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
          if scores[0][i] >= 0.5:
            mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
            mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
            apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)
            cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)

            if apx_distance <=0.5:
              if mid_x > 0.3 and mid_x < 0.7:
                cv2.putText(image_np, 'WARNING!!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
                print("Warning -Vehicles Approaching")
                engine.say("Warning -Vehicles Approaching")
                engine.runAndWait()
        
        if classes[0][i] ==44:
            if scores[0][i] >= 0.5:
                mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
                mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
                apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)
                cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
                print(apx_distance)
                engine.say(apx_distance)
                engine.say("units")
                engine.say("BOTTLE IS AT A SAFER DISTANCE")
                
                
                if apx_distance <=0.5:
                    if mid_x > 0.3 and mid_x < 0.7:
                        cv2.putText(image_np, 'WARNING!!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
                        print("Warning -BOTTLE very close to the frame")
                        engine.say("Warning -BOTTLE very close to the frame")
                        engine.runAndWait()
        if classes[0][i] ==1:
            if scores[0][i] >= 0.5:
                mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
                mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
                apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)
                cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
                print(apx_distance)
                engine.say(apx_distance)
                engine.say("units")
                engine.say("Person is AT A SAFER DISTANCE")
                
                
                if apx_distance <=0.5:
                    if mid_x > 0.3 and mid_x < 0.7:
                        cv2.putText(image_np, 'WARNING!!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
                        print("Warning -Person very close to the frame")
                        engine.say("Warning -Person very close to the frame")
                        engine.runAndWait()
                    
                
                
            
      
#      plt.figure(figsize=IMAGE_SIZE)
#      plt.imshow(image_np)
      #cv2.imshow('IPWebcam',image_np)
      cv2.imshow('image',cv2.resize(image_np,(1024,768)))
      if cv2.waitKey(1) & 0xFF == ord('t'):
          cv2.destroyAllWindows()
          cap.release()
          break