v_channel.py

import cv2
import numpy as np
import os
import json


directory = '3D_images/'
directory_raw = '3D_images_raw/'
dir_list = os.listdir(directory)


def find_centroids(image_path):
    im = cv2.imread(image_path)
    xs = []
    ys = []
    areas = []
    hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
    th, bw = cv2.threshold(hsv[:, :, 2], 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
    morph = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)
    dist = cv2.distanceTransform(morph, cv2.DIST_L2, cv2.DIST_MASK_PRECISE)
    borderSize = 3
    distborder = cv2.copyMakeBorder(dist, borderSize, borderSize, borderSize, borderSize,
                                    cv2.BORDER_CONSTANT | cv2.BORDER_ISOLATED, 0)
    gap = 1
    kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2*(borderSize-gap)+1, 2*(borderSize-gap)+1))
    kernel2 = cv2.copyMakeBorder(kernel2, gap, gap, gap, gap,
                                    cv2.BORDER_CONSTANT | cv2.BORDER_ISOLATED, 0)
    distTempl = cv2.distanceTransform(kernel2, cv2.DIST_L2, cv2.DIST_MASK_PRECISE)
    nxcor = cv2.matchTemplate(distborder, distTempl, cv2.TM_CCOEFF_NORMED)
    mn, mx, _, _ = cv2.minMaxLoc(nxcor)
    th, peaks = cv2.threshold(nxcor, mx*0.5, 255, cv2.THRESH_BINARY)
    peaks8u = cv2.convertScaleAbs(peaks)
    _, contours, hierarchy = cv2.findContours(peaks8u, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
    peaks8u = cv2.convertScaleAbs(peaks)    # to use as mask

    for i in range(len(contours)):
        x, y, w, h = cv2.boundingRect(contours[i])
        _, mx, _, mxloc = cv2.minMaxLoc(dist[y:y+h, x:x+w], peaks8u[y:y+h, x:x+w])
        area = np.pi * mx * mx

        if area >= 100:
            cv2.circle(im, (int(mxloc[0]+x), int(mxloc[1]+y)), int(mx), (255, 0, 0), 2)
            xs.append(int(mxloc[0]+x))
            ys.append(int(mxloc[1]+y))
            areas.append(float(area))
        # cv2.rectangle(im, (x, y), (x+w, y+h), (0, 255, 255), 2)
        # cv2.drawContours(im, contours, i, (0, 0, 255), 2)

    cv2.imshow('circles', im)
    cv2.waitKey(0)

    # return xs, ys, areas


def find_centroid_for_all():
    result = {}
    for filename in dir_list:
        print(filename)
        if filename[-4:] != '.jpg':
            continue
        colors = []
        xs, ys, areas = find_centroids(directory + filename)
        im = cv2.imread(directory_raw + filename, cv2.IMREAD_GRAYSCALE)
        for i in range(len(xs)):
            colors.append(im[int(ys[i])][int(xs[i])].item())

        t = int(filename[2:4])
        s = int(filename[7:10])

        if t not in result.keys():
            result[t] = {}
        result[t][s] = {}
        result[t][s]['xs'] = xs
        result[t][s]['ys'] = ys
        result[t][s]['areas'] = areas
        result[t][s]['colors'] = colors

    # import pdb
    # pdb.set_trace()
    with open('2d_blobs.json', 'w') as output_file:
        json.dump(result, output_file)


def show_centroid_for_one(filename):
    colors = []
    xs, ys, areas = find_centroids(directory + filename)
    im = cv2.imread(directory_raw + filename, cv2.IMREAD_GRAYSCALE)
    for i in range(len(xs)):
        colors.append(im[int(ys[i])][int(xs[i])])
    print(xs)
    print(ys)
    print(areas)
    print(colors)


show_centroid_for_one('t=01_z=002.jpg')
# find_centroid_for_all()