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filter.py
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filter.py
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import numpy as np
import cv2
import Global_Params
import algorithm
import threading
def RgbMedianFilter(img_set, size):
"""
对RGB图片集做中值滤波(暂时只考虑了奇数大小的窗口,如3x3、5x5)(but好像作用不大)
:param img_set: 图片集, shape = [num, height, width, 3]
:param size: 中值滤波窗口大小 (size, size)
:return: 中值滤波后的图片集
"""
num = img_set.shape[0]
height = img_set.shape[1]
width = img_set.shape[2]
filted_img_set = np.zeros((num, height, width, 3))
edge = (int)(size/2)
idx = 0
for img in img_set:
for i in range(height):
for j in range(width):
if i < edge or i > height-edge-1 or j < edge or j > width-edge-1:
filted_img_set[idx, i, j, :] = img[i, j, :]
else:
for color in range(3):
filted_img_set[idx, i, j, color] = np.median(img[i-edge:i+edge, j-edge:j+edge, color])
cv2.imshow("img", img/255.0)
cv2.imshow("filted_img", filted_img_set[idx]/255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
idx += 1
return filted_img_set
def RedBlackBoost_Original(img_set, IMAGE_SHOW_OR_NOT, IMAGE_SHOW_FREQUENCY):
'''
增强图片中的红黑色(红色棋子增强红色，黑色棋子增强黑色)
:param img_set: 图片集, shape = [num, height, width, 3]
:return: 处理后的图片集
'''
num = img_set.shape[0]
height = img_set.shape[1]
width = img_set.shape[2]
boosted_img_set = np.zeros((num, height, width, 3))
hsv_img = np.zeros((height, width, 3))
threshold = 0.02
idx = 0
count = 0
radius = Global_Params.M_norm_size/2
mid =Global_Params.M_norm_size/2.0
mid = int(mid)
for img in img_set:
count += 1
red_cnt = 0
red_flag = False
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
for i in range(height):
for j in range(width):
if ((0 <= hsv_img[i, j, 0] <= 10) or (310 <= hsv_img[i, j, 0] <= 360)) and (70 <= hsv_img[i, j, 1]*255 <= 255) and (50 <= hsv_img[i, j, 2] <= 255):
red_cnt += 1
if red_cnt >= threshold * height * width:
red_flag = True
break
if red_flag is True:
break
if red_flag is True: # 红棋子红色增强
for i in range(height):
for j in range(width):
if algorithm.outOfRadius(j, i):
boosted_img_set[idx, i, j, :] = [255, 255, 255]
continue
if ((0 <= hsv_img[i, j, 0] <= 20) or (100 <= hsv_img[i, j, 0] <= 360)) and \
(66.0 <= hsv_img[i, j, 1]*255 <= 255) and (33.0 <= hsv_img[i, j, 2] <= 255):
boosted_img_set[idx, i, j, :] = [0, 0, 255]
else:
boosted_img_set[idx, i, j, :] = [255, 255, 255]
else: # 黑棋子黑色增强
for i in range(height):
for j in range(width):
if algorithm.outOfRadius(j, i):
boosted_img_set[idx, i, j, :] = [255, 255, 255]
continue
if (0 <= hsv_img[i, j, 0] <= 360) and \
(0 <= hsv_img[i, j, 1]*255 <= 255) and (0 <= hsv_img[i, j, 2] <= 70):
boosted_img_set[idx, i, j, :] = [0, 0, 0]
else:
boosted_img_set[idx, i, j, :] = [255, 255, 255]
if idx/30 == int(idx/30):
print("RedBlackBoost Processing... <cur idx =", idx, ">")
if IMAGE_SHOW_OR_NOT and idx/IMAGE_SHOW_FREQUENCY == int(idx/IMAGE_SHOW_FREQUENCY):
print("hsv_img[mid, mid, :] -> ", hsv_img[mid, mid, :])
cv2.imshow("img_" + str(idx), img/255.0)
cv2.imshow("boosted_img_" + str(idx), boosted_img_set[idx]/255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
idx += 1
del img_set
return boosted_img_set
# def RedBlackBoost_SingleThread(img_set, IMAGE_SHOW_OR_NOT, IMAGE_SHOW_FREQUENCY, thread_idx):
# '''
# 增强图片中的红黑色(红色棋子增强红色，黑色棋子增强黑色)
# :param img_set: 图片集, shape = [num, height, width, 3]
# :return: 处理后的图片集
# '''
# num = img_set.shape[0]
# print("num =", num, "\t\tthread_idx =", thread_idx)
# height = img_set.shape[1]
# width = img_set.shape[2]
# boosted_img_set = np.zeros((num, height, width, 3))
# hsv_img = np.zeros((height, width, 3))
# threshold = 0.02
# idx = 0
# count = 0
# radius = Global_Params.M_norm_size/2
# mid =Global_Params.M_norm_size/2.0
# mid = int(mid)
# for img in img_set:
# count += 1
# red_cnt = 0
# red_flag = False
# hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# for i in range(height):
# for j in range(width):
# if ((0 <= hsv_img[i, j, 0] <= 10) or (310 <= hsv_img[i, j, 0] <= 360)) and (70 <= hsv_img[i, j, 1]*255 <= 255) and (50 <= hsv_img[i, j, 2] <= 255):
# red_cnt += 1
# if red_cnt >= threshold * height * width:
# red_flag = True
# break
# if red_flag is True:
# break
# if red_flag is True: # 红棋子红色增强
# for i in range(height):
# for j in range(width):
# if algorithm.outOfRadius(j, i):
# boosted_img_set[idx, i, j, :] = [255, 255, 255]
# continue
# if ((0 <= hsv_img[i, j, 0] <= 20) or (100 <= hsv_img[i, j, 0] <= 360)) and \
# (66.0 <= hsv_img[i, j, 1]*255 <= 255) and (33.0 <= hsv_img[i, j, 2] <= 255):
# boosted_img_set[idx, i, j, :] = [0, 0, 255]
# else:
# boosted_img_set[idx, i, j, :] = [255, 255, 255]
# else: # 黑棋子黑色增强
# for i in range(height):
# for j in range(width):
# if algorithm.outOfRadius(j, i):
# boosted_img_set[idx, i, j, :] = [255, 255, 255]
# continue
# if (0 <= hsv_img[i, j, 0] <= 360) and \
# (0 <= hsv_img[i, j, 1]*255 <= 255) and (0 <= hsv_img[i, j, 2] <= 70):
# boosted_img_set[idx, i, j, :] = [0, 0, 0]
# else:
# boosted_img_set[idx, i, j, :] = [255, 255, 255]
# if idx/30 == int(idx/30):
# print("RedBlackBoost Processing... <cur idx =", idx, ">, <thread idx =", thread_idx, ">")
# if IMAGE_SHOW_OR_NOT and idx/IMAGE_SHOW_FREQUENCY == int(idx/IMAGE_SHOW_FREQUENCY):
# print("hsv_img[mid, mid, :] -> ", hsv_img[mid, mid, :])
# cv2.imshow("img_" + str(idx), img/255.0)
# cv2.imshow("boosted_img_" + str(idx), boosted_img_set[idx]/255.0)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# idx += 1
# del img_set
# return boosted_img_set
# print("RedBlackBoost thread #", thread_idx, "finished!")
def distribution(NUM_IMG, THREAD):
print("num =", NUM_IMG, "\t\tthread =", THREAD)
reminder = NUM_IMG%THREAD
base = round((NUM_IMG - reminder)/THREAD)
print("base =", base, "\t\treminder =", reminder)
dis_list = [base for _ in range(THREAD)]
idx_list = [0 for _ in range(THREAD)]
for idx in range(reminder):
dis_list[idx] += 1
for idx in range(THREAD):
if idx > 0:
idx_list[idx] += dis_list[idx - 1] + idx_list[idx - 1]
else:
idx_list[idx] = 0
print("distribution list =", dis_list)
print("dis index list =", idx_list)
return dis_list, idx_list
def RedBlackBoost(img_set, IMAGE_SHOW_OR_NOT, IMAGE_SHOW_FREQUENCY, THREAD=12):
num = img_set.shape[0]
height = img_set.shape[1]
width = img_set.shape[2]
boosted_img_set_total = np.zeros((num, height, width, 3))
distribution_list, dis_index_list = distribution(num, THREAD)
# return
def RedBlackBoost_SingleThread(img_set, IMAGE_SHOW_OR_NOT, IMAGE_SHOW_FREQUENCY, thread_idx):
'''
增强图片中的红黑色(红色棋子增强红色，黑色棋子增强黑色)
:param img_set: 图片集, shape = [num, height, width, 3]
:return: 处理后的图片集
'''
num = img_set.shape[0]
print("num =", num, "\t\tthread_idx =", thread_idx, "\t\tdistribution_list[thread_idx] =", distribution_list[thread_idx])
height = img_set.shape[1]
width = img_set.shape[2]
boosted_img_set = np.zeros((num, height, width, 3))
hsv_img = np.zeros((height, width, 3))
threshold = 0.02
idx = 0
count = 0
radius = Global_Params.M_norm_size/2
mid =Global_Params.M_norm_size/2.0
mid = int(mid)
for img in img_set:
count += 1
red_cnt = 0
red_flag = False
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
for i in range(height):
for j in range(width):
if ((0 <= hsv_img[i, j, 0] <= 10) or (310 <= hsv_img[i, j, 0] <= 360)) and (70 <= hsv_img[i, j, 1]*255 <= 255) and (50 <= hsv_img[i, j, 2] <= 255):
red_cnt += 1
if red_cnt >= threshold * height * width:
red_flag = True
break
if red_flag is True:
break
if red_flag is True: # 红棋子红色增强
for i in range(height):
for j in range(width):
if algorithm.outOfRadius(j, i):
boosted_img_set[idx, i, j, :] = [255, 255, 255]
continue
if ((0 <= hsv_img[i, j, 0] <= 20) or (100 <= hsv_img[i, j, 0] <= 360)) and \
(66.0 <= hsv_img[i, j, 1]*255 <= 255) and (33.0 <= hsv_img[i, j, 2] <= 255):
boosted_img_set[idx, i, j, :] = [0, 0, 255]
else:
boosted_img_set[idx, i, j, :] = [255, 255, 255]
else: # 黑棋子黑色增强
for i in range(height):
for j in range(width):
if algorithm.outOfRadius(j, i):
boosted_img_set[idx, i, j, :] = [255, 255, 255]
continue
if (0 <= hsv_img[i, j, 0] <= 360) and \
(0 <= hsv_img[i, j, 1]*255 <= 255) and (0 <= hsv_img[i, j, 2] <= 70):
boosted_img_set[idx, i, j, :] = [0, 0, 0]
else:
boosted_img_set[idx, i, j, :] = [255, 255, 255]
if idx/30 == int(idx/30):
print("RedBlackBoost Processing... <cur idx =", idx, ">, <thread idx =", thread_idx, ">")
if IMAGE_SHOW_OR_NOT and idx/IMAGE_SHOW_FREQUENCY == int(idx/IMAGE_SHOW_FREQUENCY):
print("hsv_img[mid, mid, :] -> ", hsv_img[mid, mid, :])
cv2.imshow("img_" + str(idx), img/255.0)
cv2.imshow("boosted_img_" + str(idx), boosted_img_set[idx]/255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
idx += 1
del img_set
# return boosted_img_set
boosted_img_set_total[dis_index_list[thread_idx]:dis_index_list[thread_idx] + distribution_list[thread_idx]] = boosted_img_set
print("RedBlackBoost thread #", thread_idx, "finished!")
threads = [threading.Thread(target=RedBlackBoost_SingleThread, \
args=(img_set[dis_index_list[i]:dis_index_list[i] + distribution_list[i]], \
IMAGE_SHOW_OR_NOT, IMAGE_SHOW_FREQUENCY, i)) \
for i in range(THREAD)]
for i in range(THREAD):
threads[i].start()
for i in range(THREAD):
threads[i].join()
# cv2.imshow("img_" + str(99), img_set[99]/255.0)
# cv2.imshow("boosted_img_" + str(99), boosted_img_set_total[99]/255.0)
# flag = cv2.waitKey(0)
# if flag == 27:
# cv2.destroyAllWindows()
# return
return boosted_img_set_total
"""
num = img_set.shape[0]
height = img_set.shape[1]
width = img_set.shape[2]
boosted_img_set = np.zeros((num, height, width, 3))
hsv_img = np.zeros((height, width, 3))
threshold = 0.02
idx = 0
count = 0
radius = Global_Params.M_norm_size/2
mid =Global_Params.M_norm_size/2.0
mid = int(mid)
"""