refine accuracy

import circle_fit

import os

import json

from skimage.filters import threshold_otsu

input_img = cv2.imread('1492627187263118217/1.jpg')

def transform(input_img, num):

    rows, cols, ch = input_img.shape

    print(input_img.shape)

    # rows, cols, ch = input_img.shape

    # print(input_img.shape)

    # pts1 = np.float32([[629,250],[0,445],[705,250],[1279,430]])

    pts1 = np.float32([[450, 300], [-851, 720], [870, 300], [2057, 720]])

    M = cv2.getPerspectiveTransform(pts1, pts2)

    dst = cv2.warpPerspective(input_img, M, (600, 600))

    dst_extract = abs_thresh(dst, sobel_kernel=3, mag_thresh=(45, 210), direction='x')

    dst_extract = abs_thresh(dst, sobel_kernel=3, mag_thresh=(35, 210), direction='x')

    # dst_extract = abs_otsu(dst)

    # dst_extract = np.where(dst_extract>0.5,1,0)

    # histogram = np.sum(dst_extract[dst_extract.shape[0] // 2:, :], axis=0)

    # plt.imshow(dst), plt.title(foldername + filename), plt.savefig("ware_dataset/op/" + foldername + "_" + filename + "_transform.png", dpi=300)

    ##add line mark

    dst_mark = add_line_mark(dst_extract)

    dst_mark = add_line_mark(dst_extract,dst)

    # plt.subplot(131), plt.imshow(input_img), plt.title('Input')

    # plt.subplot(132), plt.imshow(dst), plt.title('Output')

    # plt.show()

def abs_thresh(img, sobel_kernel=9, mag_thresh=(0, 255), return_grad=False, direction='x'):

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    return grad_binary

def abs_otsu(img):

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    thresh = threshold_otsu(gray)

    binary = gray > thresh

    return binary

def add_line_mark(input_img):

def add_line_mark(input_img,dst):

    # determined start point

    # delete triangle

    # fill blank

    input_img_hist_shape = input_img[400:599, 0:599]

    # plt.plot(histogram)

    # plt.show()

    # use window to find points (windows size: H30 W50)

    def gen_windows(bottom_H, midpoint_W, H, W):

        find_indicator = 1

        window = input_img[bottom_H - 30:bottom_H, midpoint_W - 50:midpoint_W + 50]

        H = 30

        # window_arr = input_img[]

        histogram_window = np.sum(window[window.shape[0] // 2:, :], axis=0)

        window_peak = find_peaks(histogram_window, distance=100)[0]

        return_mid = 0

        if window_peak.size > 0:

            if histogram_window(window_peak) > 10 and (window_peak - (W / 2) < 100 ):

            if histogram_window[window_peak] > 5 and np.abs(window_peak - (W / 2)) < 50:

                return_mid = midpoint_W - (W / 2) + window_peak

            else:

                return_mid = midpoint_W

        else:

            return_mid = midpoint_W

            find_indicator = 0

        # plt.plot(histogram_window)

        # plt.show()

    # gen_windows(299,peak[0],30,50)

    plt.imshow(input_img)

    #plt.imshow(dst)

    input_img_hist_all = np.sum(input_img[input_img.shape[0] // 2:, :], axis=0)

    peak_hist_all = find_peaks(input_img_hist_all, distance=150)

    print(peak_hist_all)

    # iteraton(L)

    # print(peak_L)

    try:

        midpoint_arr_L = find_point(0)

        print(midpoint_arr_L.T)

        plt.scatter(x=midpoint_arr_L.T[0], y=midpoint_arr_L.T[1])

        ycl, xcl, rcl, _ = circle_fit.hyper_fit(midpoint_arr_L)

        if rcl<300:

            accuracy=0

        plt.gca().add_artist(plt.Circle((ycl, xcl), rcl, color='r', fill=False))

    except Exception:

        accuracy = 0

        pass

    try:

    # plt.title(str(i))

    # plt.savefig("op/op_reg" + str(i) + ".png", dpi=300)

    plt.title(foldername + filename)

    plt.savefig("op/" + foldername + "_" + filename + ".png", dpi=300)

    # plt.savefig("op/" + foldername + "_" + filename + ".png", dpi=300)

    plt.savefig("ware_dataset/op/" + foldername + "_" + filename + ".png", dpi=300)

    plt.close()

    return 0

rootdir = 'D:/train_set/clips/0531'

# rootdir = 'D:/train_set/clips/0531'

rootdir = 'D:/train_set/clips/examine'

foldername = "null"

accuracy_arr = []

for subdir, dirs, files in os.walk(rootdir):

    for file in files:

        impath = os.path.join(subdir, file)

        input_img = cv2.imread(impath)

        input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)

        input_img = input_img[:,:,0]

        foldername = subdir[-19:]

        filename = file.split(".")[0]

        i = 0

        accuracy = 1

        transform(input_img, i)

        # print(accuracy)

        accuracy_arr.append(accuracy)

jsonString = json.dumps(accuracy)

with open('accuracy.json', 'w') as outfile:

    json.dump(jsonString, outfile)

# jsonString = json.dumps(accuracy)

# with open('accuracy.json', 'w') as outfile:

#     json.dump(jsonString, outfile)

# for i in range(1, 20):

#     input_img = cv2.imread('1492626805094402903/' + str(i) + '.jpg')