__result__.py

import numpy as np
import sys
from PIL import Image, ImageDraw
from matplotlib import pyplot as plt


def detect(model, path):
    image = Image.open(path)
    pix = image.load()

    n_spectrum = 3
    width = image.size[0]
    height = image.size[1]

    # creat vector
    picture_vector = []
    for chanel in range(n_spectrum):
        for y in range(height):
            for x in range(width):
                picture_vector.append(pix[x, y][chanel])

    picture_vector = np.array(picture_vector).astype(
        "uint8"
    )
    picture_tensor = picture_vector.reshape(
        [n_spectrum, height, width]
    ).transpose(1, 2, 0)

    picture_tensor = picture_tensor.transpose(2, 0, 1)

    step = 10
    coordinates = []
    for y in range(int((height - (80 - step)) / step)):
        for x in range(int((width - (80 - step)) / step)):
            area = cutting(
                x * step, y * step, picture_tensor
            )
            result = model.predict(area)
            if result[0][1] > 0.90 and not_near(
                x * step, y * step, 88, coordinates
            ):
                coordinates.append(
                    [[x * step, y * step], result]
                )
                print(result)

    for e in coordinates:
        x = e[0][0]
        y = e[0][1]
        acc = e[1][0][1]
        thickness = 5
        for i in range(80):
            for ch in range(3):
                for th in range(thickness):
                    picture_tensor[ch][y + i][x - th] = -1

        for i in range(80):
            for ch in range(3):
                for th in range(thickness):
                    picture_tensor[ch][y + i][
                        x + th + 80
                    ] = -1

        for i in range(80):
            for ch in range(3):
                for th in range(thickness):
                    picture_tensor[ch][y - th][x + i] = -1

        for i in range(80):
            for ch in range(3):
                for th in range(thickness):
                    picture_tensor[ch][y + th + 80][
                        x + i
                    ] = -1

    # picture_tensor = picture_tensor.transpose(2,0,1)
    picture_tensor = picture_tensor.transpose(1, 2, 0)

    plt.figure(1, figsize=(15, 30))

    plt.subplot(3, 1, 1)
    plt.imshow(picture_tensor)

    plt.show()


def cutting(x, y, picture_tensor):
    area_study = np.arange(3 * 80 * 80).reshape(3, 80, 80)
    for i in range(80):
        for j in range(80):
            area_study[0][i][j] = picture_tensor[0][y + i][
                x + j
            ]
            area_study[1][i][j] = picture_tensor[1][y + i][
                x + j
            ]
            area_study[2][i][j] = picture_tensor[2][y + i][
                x + j
            ]
    area_study = area_study.reshape([-1, 3, 80, 80])
    area_study = area_study.transpose([0, 2, 3, 1])
    area_study = area_study / 255
    sys.stdout.write("\rX:{0} Y:{1}  ".format(x, y))
    return area_study


def not_near(x, y, s, coordinates):
    result = True
    for e in coordinates:
        if (
            x + s > e[0][0]
            and x - s < e[0][0]
            and y + s > e[0][1]
            and y - s < e[0][1]
        ):
            result = False
    return result