def pretreate_image(im, image_name):
"""对图像进行预处理
:param im:
需要预处理的图像
:return:
无
"""
threshold = 180
image_binary = image_binaryzation(im, threshold)
image_binary_pixels = list(image_binary.getdata())
im_rgb = im.convert('RGB')
list_rgb_pixels = list(im_rgb.getdata())
image_rgb_pixels = list_rgb_pixels.copy()
width, height = im.size
remove_noise(image_rgb_pixels, image_binary_pixels, width, height)
im_rgb.putdata(image_rgb_pixels)
im_rgb.save(image_name + "_without_noise.jpg")
list_pixels_for_statistic = image_rgb_pixels.copy() # 用于统计的像素list，接下来要将背景色像数去掉
remove_background_pixels(list_pixels_for_statistic)
# pixels_statistic = {}
pixels_statistic_largest_4 = {} # 图中最多的的四种颜色 / The 4 colors with the largest area
least_color_value = 0 # 四种颜色中，最小那种颜色的面积 / The area of the color with the minimal area
for color in list_pixels_for_statistic:
# if color not in pixels_statistic:
area = list_pixels_for_statistic.count(color)
# pixels_statistic.setdefault(color, area)
if len(pixels_statistic_largest_4) < 4:
pixels_statistic_largest_4.setdefault(color, area)
least_color_key, least_color_value = CustomDataStructureUtil.get_min(pixels_statistic_largest_4)
else:
if color not in pixels_statistic_largest_4:
if area > least_color_value:
pixels_statistic_largest_4.pop(least_color_key)
pixels_statistic_largest_4.setdefault(color, area)
least_color_key, least_color_value = CustomDataStructureUtil.get_min(pixels_statistic_largest_4)
print(CustomDataStructureUtil.get_min(pixels_statistic_largest_4)[1])
print(pixels_statistic_largest_4)
largest_colors = list(pixels_statistic_largest_4.keys())
image_pixels = image_rgb_pixels.copy()
for large_color in largest_colors:
get_single_color_image(im_rgb.copy(), image_pixels.copy(), large_color, width, height)
