import base64
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from skimage import measure, morphology, color, segmentation
import io

def get_projected_area(image, area_threshold, display_threshold):
    total_px = image.size
    total_mm = 60322.46

    output = []
    original = cv.imdecode(image, cv.IMREAD_UNCHANGED)
    original = cv.cvtColor(original, cv.COLOR_BGR2RGB)

    img = cv.cvtColor(original, cv.COLOR_BGR2GRAY)
    _retval, thresh_gray = cv.threshold(img, 200, 255, cv.THRESH_BINARY)

    img = morphology.area_closing(thresh_gray, area_threshold=area_threshold, connectivity=1)

    contours = measure.find_contours(array=img, level=100)

    fig, ax = plt.subplots()
    ax.imshow(original, cmap=plt.cm.gray, alpha=0.3)

    index = 1

    for contour in contours:
        area = calculate_area(contour)
        
        if calculate_area(contour) > display_threshold:
            ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')

            cX, cY = center_of_mass(contour)
            plt.text(cY, cX, index, color='black', fontsize=6)
            
            output.append((index, round(area / total_px * total_mm, 2)))

            # print(area, total_px)


            index += 1

    ax.axis('image')
    ax.set_xticks([])
    ax.set_yticks([])
    
    ax.margins(0)

    my_stringIObytes = io.BytesIO()
    plt.savefig(my_stringIObytes, format='png', dpi=500, bbox_inches='tight')
    my_stringIObytes.seek(0)
    image_arr = base64.b64encode(my_stringIObytes.read()).decode(encoding='utf-8')

    return image_arr, output

def calculate_area(countour):
    c = np.expand_dims(countour.astype(np.float32), 1)
    c = cv.UMat(c)
    
    return cv.contourArea(c)

def center_of_mass(X):
    x = X[:,0]
    y = X[:,1]
    g = (x[:-1]*y[1:] - x[1:]*y[:-1])
    A = 0.5*g.sum()
    cx = ((x[:-1] + x[1:])*g).sum()
    cy = ((y[:-1] + y[1:])*g).sum()

    return 1./(6*A)*np.array([cx,cy])