# Libraries
import streamlit as st
from tensorflow import keras
import tensorflow as tf
from PIL import Image
import numpy as np
import cv2
import matplotlib.pyplot as plt
from imutils import perspective
from scipy.spatial import distance as dist
import os

# Constants
MODEL_PATH = 'model.h5'
IMAGE_DIR = 'images'

# Functions 
def load_image(image_file):
    img = Image.open(image_file)
    return img

def midpoint(ptA, ptB):
    return ((ptA[0] + ptB[0]) /2 , (ptA[1] + ptB[1]) /2)

def draw_dimensions(orig_image,predict_image,erode_iteration,open_iteration):
    kernel1 =( np.ones((5,5), dtype=np.float32))#
    kernel_sharpening = np.array([[-1,-1,-1], 
                                  [-1,9,-1], 
                                 [-1,-1,-1]])#
                                 
    image = predict_image
    image2 = orig_image 

    image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel1,iterations=open_iteration )
    image = cv2.filter2D(image, -1, kernel_sharpening)
    image = cv2.erode(image,kernel1,iterations =erode_iteration)

    image=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)#original

    thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    labels=cv2.connectedComponents(thresh,connectivity=8)[1]       
    a=np.unique(labels)
    count2=0
    for label in a:
        if label == 0:
            continue
    
        # Create a mask
        mask = np.zeros(thresh.shape, dtype="uint8")
        mask[labels == label] = 255
        # Find contours and determine contour area
        cnts,hieararch = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnts = cnts[0]
        c_area = cv2.contourArea(cnts)
        
        (x,y),radius = cv2.minEnclosingCircle(cnts)
        rect = cv2.minAreaRect(cnts)
        box = cv2.boxPoints(rect)
        box = np.array(box, dtype="int")    
        box = perspective.order_points(box)
        color1 = (list(np.random.choice(range(150), size=3)))  
        color =[int(color1[0]), int(color1[1]), int(color1[2])]  
        cv2.drawContours(image2,[box.astype("int")],0,color,2) 
        (tl,tr,br,bl)=box
        
        (tltrX,tltrY)=midpoint(tl,tr)
        (blbrX,blbrY)=midpoint(bl,br)
    	# compute the midpoint between the top-left and top-right points,
    	# followed by the midpoint between the top-righ and bottom-right
        (tlblX,tlblY)=midpoint(tl,bl)
        (trbrX,trbrY)=midpoint(tr,br)
    	# draw the midpoints on the image
        cv2.circle(image2, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
        cv2.circle(image2, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
        cv2.circle(image2, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
        cv2.circle(image2, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
        cv2.line(image2, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),color, 2)
        cv2.line(image2, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),color, 2)
        dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
        dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))

        global dimA
        dimA = dA*0.08
        global dimB
        dimB = dB*0.08
        cv2.putText(image2, "{:.1f} millimeter".format(dimA),(int(tltrX - 10), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,0.65, (0,0,0), 2)
        cv2.putText(image2, "{:.1f} millimeter".format(dimB),(int(trbrX + 10), int(trbrY+10)), cv2.FONT_HERSHEY_SIMPLEX,0.65,(0,0,0), 2)

    return image2 

def segment_molar(image_file):

    img=load_image(image_file)
      
    img = np.asarray(img)     
    img = cv2.resize(img, (512, 256))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    img = np.expand_dims(img, axis=0)

    prediction = model.predict(img)

    output = prediction.reshape(256,512)

    return img , output

def measure_molar(image_file):

    img=load_image(image_file)
      
    img = np.asarray(img) 
    img_prd= cv2.resize(img , (512, 256))
    img_prd = cv2.cvtColor(img_prd, cv2.COLOR_RGB2GRAY)
    img_prd = np.expand_dims(img_prd, axis=0)
    prediction = model.predict(img_prd)
    prediction=prediction*255
    prediction = prediction.astype("uint8")
    prediction_img=prediction.reshape(256,512)
    img2 = np.zeros( ( np.array(prediction_img).shape[0], np.array(prediction_img).shape[1], 3 ) )
    img2[:,:,0] = prediction_img 
    img2[:,:,1] = prediction_img
    img2[:,:,2] = prediction_img
    img2 = img2.astype("uint8")
    predicted = cv2.resize(img2 , (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
    output=draw_dimensions(img,predicted,3,2)

    return output

# Load model and images
model = keras.models.load_model(MODEL_PATH)
images = os.listdir(IMAGE_DIR)
images = [f'{IMAGE_DIR}/{image}' for image in images]

# Labels
labels = {'fatemeh_hafari_27022022_181119right.png': 'classI',
 'majid_mohammadkhanlu_19022022_175338right.png': 'mesioangular',
 'zahra_khodabandeh_17052022_162804right.png': 'mesioangular',
 '133028019739531250right.png': 'classII',
 'khadijeh_fard_aghababaei_03012022_120330right.png': 'B',
 'moharam_ali_khamseh_01112021_194329right.png': 'B',
 'parniya_mohamadinasab_08112021_184639right.png': 'B',
 'afsaneh_khalaji_24022022_183000right.png': 'B',
 'sogand_rahmani_13072022_113636right.png': 'B',
 'fatemeh_mirzaei_13072022_101734right.png': 'mesioangular',
 'fatemeh_hoseini_24012022_111330right.png': 'C',
 'padisar_ebrahimi_13122021_111512right.png': 'mesioangular'}

# UserInterface
st.header("3rdMolar Segmentation")
st.subheader("Select Image:")
image_file = st.selectbox('Select Image' , images)

if image_file is not None:

    img , output1 = segment_molar(image_file)
    output2 = measure_molar(image_file)
    
    st.subheader(f"Label : {labels[image_file.split('/')[1]]}")
    st.image(img[0],width=850)
    st.image(output1,width=850)
    st.image(output2,width=850)