import cv2
import os
import numpy as np
from copy import copy

clicks = []
click_counter = 0


def _click_callback(event, x, y, flags, param):
    global click_counter
    global clicks
    if event == cv2.EVENT_LBUTTONDOWN:
        clicks.append((x, y))
        click_counter -= 1


def click_on_image(
        image, times=1, mark=False, delay=100,
        text='Click on image', counter=True):
    '''
    Prompt the user with a window where he/she can click predetermined
    times to complete action.

    @params:
    - image: np.array, image to show the user
    - times: integer, number of clicks requested from the user
    - mark: boolean, leave a mark on the image where clicked
    - delay: integer, refresh rate - how many milliseconds image is shown
            before refresh
    - text: string, text to show on the window
    '''
    img = image.copy()
    if len(img.shape) == 2:
        img = np.stack([img] * 3, -1)
    scale = get_scale(img)
    global clicks
    global click_counter
    clicks = []
    click_counter = times
    while click_counter > 0:
        if mark is not None and len(clicks) > 0:
            cv2.circle(
                img,
                (int(clicks[-1][0] / scale), int(clicks[-1][1] / scale)),
                int(img.shape[0] / 100.0), (255, 255, 255), 3)
            cv2.circle(
                img,
                (int(clicks[-1][0] / scale), int(clicks[-1][1] / scale)),
                int(img.shape[0] / 100.0), (0, 0, 0), -1)

        if counter:
            t = '(%s/%s) - %s' % (times - click_counter, times, text)
        else:
            t = text
        cv2.namedWindow(t)
        cv2.setMouseCallback(t, _click_callback)
        k = show_image(img, text=t, destroy=False, time=delay)
        if k != -1:
            cv2.destroyAllWindows()
            return k
    cv2.destroyAllWindows()
    out_clicks = copy(clicks)
    clicks = []
    out_clicks = np.asarray(np.array(out_clicks) / scale, dtype=int)
    return out_clicks


def get_scale(image, max_dimensions=(750, 1200)):
    '''
    Given an image and maximum display dimensions,
    get_scale returns a scale factor corresponding to the
    optimal image size reduction to fit to the screen.
    '''
    dims = image.shape
    if 0 in dims:
        raise TypeError('Cannot show image without dimensions')
    scale = min([
        float(max_dimensions[0]) / dims[0],
        float(max_dimensions[1]) / dims[1]])
    return scale


def show_image(image, text='Image', time=0, destroy=True):
    if os.uname()[-1] == 'armv7l':
        return
    scale = get_scale(image)
    resized_image = image.copy()
    resized_image = cv2.resize(resized_image, None, fx=scale, fy=scale)
    cv2.imshow(str(text), resized_image)
    k = cv2.waitKey(time)
    if destroy:
        cv2.destroyAllWindows()
    return k

import cv2
import os
import numpy as np
from copy import copy

clicks = []
click_counter = 0


def _click_callback(event, x, y, flags, param):
    global click_counter
    global clicks
    if event == cv2.EVENT_LBUTTONDOWN:
        clicks.append((x, y))
        click_counter -= 1


def click_on_image(
        image, times=1, mark=False, delay=100,
        text='Click on image', counter=True):
    '''
    Prompt the user with a window where he/she can click predetermined
    times to complete action.

    @params:
    - image: np.array, image to show the user
    - times: integer, number of clicks requested from the user
    - mark: boolean, leave a mark on the image where clicked
    - delay: integer, refresh rate - how many milliseconds image is shown
            before refresh
    - text: string, text to show on the window
    '''
    img = image.copy()
    if len(img.shape) == 2:
        img = np.stack([img] * 3, -1)
    scale = get_scale(img)
    global clicks
    global click_counter
    clicks = []
    click_counter = times
    while click_counter > 0:
        if mark is not None and len(clicks) > 0:
            cv2.circle(
                img,
                (int(clicks[-1][0] / scale), int(clicks[-1][1] / scale)),
                int(img.shape[0] / 100.0), (255, 255, 255), 3)
            cv2.circle(
                img,
                (int(clicks[-1][0] / scale), int(clicks[-1][1] / scale)),
                int(img.shape[0] / 100.0), (0, 0, 0), -1)

        if counter:
            t = '(%s/%s) - %s' % (times - click_counter, times, text)
        else:
            t = text
        cv2.namedWindow(t)
        cv2.setMouseCallback(t, _click_callback)
        k = show_image(img, text=t, destroy=False, time=delay)
        if k != -1:
            cv2.destroyAllWindows()
            return k
    cv2.destroyAllWindows()
    out_clicks = copy(clicks)
    clicks = []
    out_clicks = np.asarray(np.array(out_clicks) / scale, dtype=int)
    return out_clicks


def get_scale(image, max_dimensions=(750, 1200)):
    '''
    Given an image and maximum display dimensions,
    get_scale returns a scale factor corresponding to the
    optimal image size reduction to fit to the screen.
    '''
    dims = image.shape
    if 0 in dims:
        raise TypeError('Cannot show image without dimensions')
    scale = min([
        float(max_dimensions[0]) / dims[0],
        float(max_dimensions[1]) / dims[1]])
    return scale


def show_image(image, text='Image', time=0, destroy=True):
    if os.uname()[-1] == 'armv7l':
        return
    scale = get_scale(image)
    resized_image = image.copy()
    resized_image = cv2.resize(resized_image, None, fx=scale, fy=scale)
    cv2.imshow(str(text), resized_image)
    k = cv2.waitKey(time)
    if destroy:
        cv2.destroyAllWindows()
    return k

Prompt the user with a window where he/she can click predetermined     times to complete action.      @params:     - image: np.array, image to show the user     - times: integer, number of clicks requested from the user     - mark: boolean, leave a mark on the image where clicked     - delay: integer, refresh rate - how many milliseconds image is shown             before refresh     - text: string, text to show on the window Given an image and maximum display dimensions,     get_scale returns a scale factor corresponding to the     optimal image size reduction to fit to the screen.