title: How to take the input as an image instead of taking input from the camera in python
body: <p><strong>I want to take the input as an image instead of taking input from the camera. in this code i am taking the input from the camera. But i want to take the input from an image which is in a folder. How to do that. Here is my code</strong></p>
  <p>I am going to input images for pre trained model and get the output. This is a OCR project. I have trained the model. In this code the input image is getting from the camera. But i want to give the image from a file, not from the camera. How to do that....</p>
  <pre><code>import numpy as np
  import cv2
  import pickle
  from tensorflow.python.keras.models import load_model
  
  
  ###parameterrs###
  
  width = 640
  height = 480
  threshold = 0.65
  #threshold means minimum probability to classify
  
  #this is the code for creatinng the image objrct
  imageObj=cv2.imread(&quot;SinhalaDataSet/1/img0_0.png&quot;)
  
  #this is the code for creatinng the camera objrct
  
  capture = cv2.VideoCapture(0)
  capture.set(3,width)
  capture.set(4,height)
  
  
  
  #here im loading the saved pretrained model
  model = load_model('model.h5')
  
  #thid is the code for processing
  def preProcessing(img):
      img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
      img = cv2.equalizeHist(img)
      img = img/255
      return img
  
  while True:
      success, imgOriginal = capture.read()
      img = np.asarray(imgOriginal)
      img=cv2.resize(img,(32,32))
      img = preProcessing(img)
      cv2.imshow(&quot;Processsed Image&quot;,img)
      img = img.reshape(1, 32, 32, 1)
      #prediction
      classIndex = int(model.predict_classes(img))
  
      labelDictionary = {0: '0',    1: 'අ',   2: 'ඉ',  3: 'ඊ',   4: 'උ',  5: 'එ',  6: 'ඒ',    7: 'ඔ',    8: 'ක', 9: 'ක්', 10: 'කා',
                         11: 'කැ',  12: 'කෑ', 13: 'කි', 14: 'කී', 15: 'කු', 16: 'කූ', 17: 'කෙ', 18: 'කේ',  19: 'කො',
                         20: 'කෝ', 21: 'ඛ', 22: 'ග',  23: 'ගි', 24: 'ගී', 25: 'ගු',  26: 'ගූ',  27: 'ඝ',   28: 'ඟ', 29: 'ච',
                         30: 'ඡ',   31: 'ජ', 32: 'ජ්',  33: 'ජි', 34: 'ජී', 35: 'ඣ', 36: 'ඤ',  37: 'ඥ',  38: 'ට', 39: 'ඨ',
                         40: 'ඩ',   41: 'ඪ', 42: 'ණ', 43: 'ඬ', 44: 'ත', 45: 'ත්', 46: 'ථ',   47: 'ථි',   48: 'ථී', 49: 'ද', 50: 'දු',
                         51: 'දූ',   52: 'ධ', 53: 'න',  54: 'ඳ', 55: 'ප', 56: 'පු', 57: 'පූ',   58: 'ඵ',   59: 'බ', 60: 'භ',
                         61: 'ම',   62: 'ම්', 63: 'මි',  64: 'මී', 65: 'ඹ', 66: 'ය', 67: 'ර',   68: 'ල',   69: 'ව', 70: 'ව්', 71: 'වි',
                         72: 'වී',   73: 'වු', 74: 'වූ',  75: 'ශ', 76: 'ෂ', 77: 'ස', 78: 'හ',   79: 'ළ',   80: 'ළු', 81: 'ෆ',
                         82: 'ා'}
  
      predictions = model.predict(img)
      predictedLetter = labelDictionary.get(classIndex)
      probabilityValue = np.amax(predictions)
      print(predictedLetter, probabilityValue)
  
  
  
      if probabilityValue &gt; threshold:
  
          cv2.putText(imgOriginal, str(predictedLetter) + &quot;   &quot; + str(probabilityValue),
                      (50, 50), cv2.FONT_HERSHEY_COMPLEX_SMALL,
                      1, (0, 0, 255), 1)
  
  
      cv2.imshow(&quot;Testing Window&quot;, imgOriginal)
      if cv2.waitKey(1) &amp; 0xFF == ord('q'):
          break
  
  </code></pre>