infiles = list(map(addpath, os.listdir('./photos/source/')))
statfile = open('stat_resized_pyr-2.txt', 'w')
for egg_image in infiles:
    #read and compress photo
    #
    #
    orig_img = cv2.imread(egg_image)

    scale_percent = 20
    width = int(orig_img.shape[1] * scale_percent / 100)
    height = int(orig_img.shape[0] * scale_percent / 100)
    dim = (width, height)
    img = cv2.resize(orig_img, dim, interpolation = cv2.INTER_AREA)
   # img = orig_img
    #color in range
    #
    #
    img = cv2.pyrMeanShiftFiltering(img, 120, 51)
    img = cv2.GaussianBlur(img, (7, 7), 2)
    shifted_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    shifted_hsv = cv2.cvtColor(shifted_rgb, cv2.COLOR_RGB2HSV)

    #HSV->HSV convertation
    #cv2 HSV: 0-179, 0-255, 0-255
    #paint.net HSV: 0-360, 0-100, 0-100
    lb, db = (175, 0, 80), (210, 40, 100)
    light_blue = (lb[0]/2, int(lb[1]*2.55), int(lb[2]*2.55))
    dark_blue = (db[0]/2, int(db[1]*2.55), int(db[2]*2.55))

    mask = cv2.inRange(shifted_hsv, light_blue, dark_blue)
    res_mask = cv2.bitwise_and(shifted_rgb, shifted_rgb, mask = mask)

    #res_mask_shifted = cv2.pyrMeanShiftFiltering(res_mask, 120, 51)
    #res_mask_shifted = cv2.pyrMeanShiftFiltering(res_mask, 20, 51)

    #plt.imshow(res_mask_shifted)
    #print(mask.shape)

    gray = cv2.cvtColor(res_mask,cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
    #plt.imshow(res_mask)


    # noise removal
    #
    #
    kernel = np.ones((3,3),np.uint8)
    opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 3)
    for string in opening:
        for i in range(len(string)):
            if(string[i] == 255): string[i] = 0
            else: string[i] = 255
    #plt.imshow(opening)


    # sure background area
    #
    #
    sure_bg = cv2.dilate(opening,kernel,iterations=3)
    #else: string[i] = 255
    #plt.imshow(sure_bg)

    # Finding sure foreground area
    #
    #
    dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
    ret, sure_fg = cv2.threshold(dist_transform,0.4*dist_transform.max(),255,0)

    #plt.imshow(sure_fg)
    #plt.imshow(dist_transform, 'gray')

    # Finding unknown region
    #
    #
    sure_fg = np.uint8(sure_fg)
    unknown = cv2.subtract(sure_bg,sure_fg)
    #plt.imshow(sure_fg)


    # Marker labelling
    #
    #
    %matplotlib inline
    ret, markers = cv2.connectedComponents(sure_fg)
    #print(ret)
    #plt.imshow(markers)


    # Add one to all labels so that sure background is not 0, but 1
    markers = markers+1
    # Now, mark the region of unknown with zero
    markers[unknown==255] = 0
    #plt.imshow(markers)

    #
    #
    %matplotlib inline

    import time
    markers = cv2.watershed(res_mask,markers)
    res_mask[markers == -1] = [255,0,0]
    plt.imshow(markers)
    #print(markers)
    eggs = []
    result = img.copy()
    for egg_i in range(0, ret+1):
        egg = np.zeros(shape = markers.shape)
        for i in range(len(markers)):
            for j in range(len(markers[i])):
                if(markers[i,j] == egg_i):
                    egg[i,j] = egg_i
        eggs.append(egg)

    counter = 0
    print(len(eggs))
    for i in range(2, len(eggs)):

        result1 = cv2.bitwise_and(img, img, mask=np.int8(eggs[i]))

        contours = findContours(result1)
        maxArea = 0
        ind = 0

        for c in range(len(contours)):
                area = cv2.contourArea(contours[c]) # площадь
                if(area > maxArea):
                    ind = c
                    maxArea = area
        try:
            kk = contours[ind]
        except:
            continue
        if(len(contours[ind]) < 5): continue
        (x, y), (MA, ma), angle = cv2.fitEllipse(contours[ind])
        el_area = 3.14 * MA * ma / 4
        size = MA*ma*ma*4*3.14/3
        if(el_area > area*1.2 or el_area < area*0.2): continue

        cv2.ellipse(result1, (int(x), int(y)), (int(MA/2), int(ma/2)), int(angle), startAngle = 0, endAngle = 360, color = (0,255,0), thickness = 1)
        cv2.imwrite('./photos/output_rsz_pyr-2/'+egg_image.split('/')[3][:-4]+'_result_'+str(i-1) +'.jpg',result1)

        statfile.write(egg_image.split('/')[3]+'\t')

        statfile.write('{}\t'.format(i-1))
        statfile.write('{}\t'.format(area))
        statfile.write('{}\t'.format(el_area))
        statfile.write('{}\t'.format(round(size,2)))
        statfile.write('{}\t'.format(MA))
        statfile.write('{}\n'.format(ma))
        counter += 1
    statfile.write('eggs: {}\n\n'.format(counter))
    statfile.close()
    statfile = open('stat_resized_pyr-2.txt', 'a')
statfile.close()