plor.py

def plot_final(Data,return_image=False):
    image1=cv2.imread(Data)
    image = aug.augment_image(image1)
    image=image[:,:,1]
    image[image <0.2]=0.5
    image = image / 255
    predicted  = unet.predict(image[np.newaxis,:,:])
    predicted[predicted <0.25]=0
    img = predicted[0,:,:,0]
    mean,std=cv2.meanStdDev(img)
    
    pixels = cv2.countNonZero(img)
    image_area = img.shape[0] * img.shape[1]
    area_ratio = (pixels / image_area) * 100
    img = img*255
    img[img<1]=1
    img[img>100]=255
    M= cv2.moments(img)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    if return_image:
      return img,area_ratio,std,(cX,cY)
    else:
      return area_ratio,std,(cX,cY)