#Import Required Packages
import numpy as np
import gradio as gr
#from google.colab.patches import cv2_imshow
import cv2
import matplotlib.pyplot as plt
import numpy as np
import skimage 
import imutils
from imutils import contours 
import math
def cube (v):
  return v**3
def sqrtabs (v) :
  return math.sqrt(abs(v))
def figplota(xvalues):
  fig = plt.figure()
  plt.plot(xvalues, figure=fig)
  return fig
def quant(imageinput):
  #@title Please Input the Lateral Flow Assay Image
  # read image using openCV
  #path = "/content/l1.jpg"
  image = cv2.imread(imageinput)#imageinput
  target = "PKU" 
  #print(image)
  #cv2_imshow(image)
  # Convert the image to grayscale 
  BGR2RGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  gray = cv2.cvtColor(BGR2RGB, cv2.COLOR_RGB2GRAY)
  #print(gray)
  #cv2_imshow(gray)
  # Invert the image to negative scale
  negative = cv2.bitwise_not(gray)
  negativeimage = negative.copy() #save a copy to avoid disrupting the image contour
  #print(negativeimage)
  #cv2_imshow(negativeimage)
  # Minimize the noisy effects of artificats using Gaussian blur (helps with minimizing the effect of noisy artifactual bright-spots)
  blur = cv2.GaussianBlur(negativeimage, (11, 11), 0)
  #print(blur)
  #cv2_imshow(blur)
  # Binarize Image
  threshold = float(cv2.meanStdDev(blur)[0]) + 0.6*float(cv2.meanStdDev(blur)[1])
  imgthreshold = cv2.threshold(blur, threshold, 255, cv2.THRESH_BINARY)[1]
  #print(imgthreshold)
  #cv2_imshow(image_thresh)
  # Reducing noise noise through eroding & eroding 
  imgeroding = cv2.erode(imgthreshold, None, iterations=1)
  zeronoise = cv2.dilate(imgeroding, None, iterations=1)
  #print(zeronoise)
  #cv2_imshow(zeronoise)
  # CCA the threshold Image
  import skimage.measure
  labels = skimage.measure.label(zeronoise, background=0)
  masking = np.zeros(zeronoise.shape, dtype="uint8")
  for label in np.unique(labels):
      if label == 0:
          continue
      MaskL = np.zeros(zeronoise.shape, dtype="uint8")
      MaskL[labels == label] = 255
      numPixels = cv2.countNonZero(MaskL)
      if numPixels > masking.shape[1]*3:
          masking = cv2.add(masking, MaskL)    
  #cv2_imshow(mask)
  # Find the contours and sort, please change from bottom-to-top to top-to-bottom accordingly
  contourss = cv2.findContours(masking.copy(), cv2.RETR_EXTERNAL,
      cv2.CHAIN_APPROX_SIMPLE)
  contourss = imutils.grab_contours(contourss)
  contourss = contours.sort_contours(contourss, method="bottom-to-top")[0] #change here accordingly
  final= []
  if len(contourss) > 1:
      for (i, c) in enumerate(contourss):
          # draw the bright spot on the image for the control and sample band 
          x, y, width, height = cv2.boundingRect(c)
          final.append(negativeimage[y:y+height, x:x+width])
          rect = cv2.minAreaRect(c)
          box = cv2.boxPoints(rect)
          # convert all coordinates floating point values to int
          box = np.int0(box)
          # draw a rectangle
          cv2.drawContours(image, [box], 0, (0, 0, 255), thickness=2)

  elif len(contourss) == 1:
          # draw the bright spot on the image for the control band
          for (i, c) in enumerate(contourss):
              x, y, width, height = cv2.boundingRect(c)
              final.append(negativeimage[y:y+height, x:x+width])
              rect = cv2.minAreaRect(c)
              box = cv2.boxPoints(rect)
              # convert all coordinates floating point values to int
              box = np.int0(box)
              # draw a rectangle
              cv2.drawContours(image, [box], 0, (0, 0, 255), thickness=2)

    
          
  # Return error message for unclear tests
  else :
      print("No Bands Detected")
  #print(image)
  #cv2_imshow(image)
  # generate signal ratio of sample to control band, you can change according to sorting of bands

  ratio1 = cv2.meanStdDev(final[0])[0]
  ratio=((cube(math.cos(sqrtabs(ratio1  - -0.393284)) + 2.2783713) / pow(math.cos(y), 0.20675313)) - (math.exp(math.cos(math.cos((sqrtabs(math.tan(cube(ratio1)) - (ratio1 +math.tan(math.sin(ratio1)))) / 0.44953698) * 0.9778089))) + (-2.3363407 / ratio1)))
  thresho = 20
  sig=final[0][0]
  #signal=plt.plot(sig,figure=plt.figure())
  if ratio >= thresho:
    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "Classic PKU, needs urgent medical treatment")
  elif ratio >= 2 and ratio <6:
    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "Likely PKU phenotype.")
  elif ratio >= 6 and ratio <12:
    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "PKU and dietary restriction is recommended")
  elif ratio >=12 and ratio <20:
    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "PKU and need medical attention for risk of intellectuall impairment")
  else:
    xx=str("The test band signal[" + str(ratio) + "mg/dl] shows a " + target +"-NEGATIVE test.")
  return xx,figplota(sig),cv2.resize(image, (20,60), interpolation = cv2.INTER_AREA) #cv2.resize(signal, (20,40), interpolation = cv2.INTER_AREA)#,cv2.resize(signal, (20,40), interpolation = cv2.INTER_AREA)
iface = gr.Interface(quant, gr.Image(type="filepath"), outputs=["text","plot","image"],debug=True)
iface.launch()