记录个人处理图像时，为去除图像中含有的标签框所用方法，以防以后找不到。
具体思路参考该博客：
python 去除图像中的框_python去掉矩形边框 数字图像处理
函数定义
导包：
from PIL import Image
import numpy as np
from sklearn.ensemble import IsolationForest
from collections import Counter
import cv2, os
from tqdm import tqdm加载图像
首先加载图像，使用该函数可以允许读取带有中文路径的图像
def cv_imread(filePath):
cv_img = cv2.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)
## imdecode读取的是rgb，如果后续需要opencv处理的话，需要转换成bgr，转换后图片颜色会变化
##cv_img=cv2.cvtColor(cv_img,cv2.COLOR_RGB2BGR)
return cv_img获取标签框
获取图像中不同颜色框的位置，这里采取的是HSV颜色空间方法，确定标签框的颜色阈值进行划分
def get_coordinate(img):
# 转换为HSV颜色空间
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# print(hsv)
# 定义不同颜色的HSV值范围
color1_lower = np.array([30, 80, 255])
color1_upper = np.array([40, 200, 255])
# 在HSV图像上应用颜色范围掩码
mask1 = cv2.inRange(hsv, color1_lower, color1_upper)
# 对原始图像和掩码进行位运算
result1 = cv2.bitwise_and(img, img, mask=mask1)
# 查找矩形框的轮廓
contours1, hierarchy1 = cv2.findContours(mask1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 获取轮廓对应坐标
labels = []
for contour1 in contours1:
x, y, w, h = cv2.boundingRect(contour1)
labels.append((x, y, x + w, y + h))
return labels去除标签框主体
# 判断颜色是否相似
def my_similar(a, b):
if sum([abs(x - y) for (x, y) in zip(list(a), list(b))]) / 3 < threhold:
return True
return False
# 判断颜色是否相似
def my_similar_border(a, b_s):
for b in b_s:
if sum([abs(x - y) for (x, y) in zip(list(a), list(b)[0])]) / 3 < threhold_border:
return True
return False
# 获取异常值，用于处理边框的边缘Q
def get_outlier(tmp, model):
normal_point = []
unnormal_point = []
# 将数据传递给模型进行训练
model.fit(tmp)
# 获取每个数据点的预测标签，-1 表示异常点，1 表示正常点
labels = model.predict(tmp)
# 打印每个数据点的标签
for i, label in enumerate(labels):
if label == -1:
unnormal_point.append(tmp[i])
if label == 1:
normal_point.append(tmp[i])
return normal_point, unnormal_point
# 在水平或处置方向上获取邻居节点，以便进行颜色替换
def get_right_neighborhood(target_color, neighborhood_x, neighborhood_y):
diff_x = 0
diff_y = 0
for neighborhood in neighborhood_x:
diff_x += abs(sum([x - y for x, y in zip(list(neighborhood), list(target_color))]) / 3)
for neighborhood in neighborhood_y:
diff_y += abs(sum([x - y for x, y in zip(list(neighborhood), list(target_color))]) / 3)
if diff_x > diff_y:
return neighborhood_x, 1
return neighborhood_y, 2
def replace_color_around_point(image, x, y, x_1, y_1, x_2, y_2, radius):
# 采样步数
step = 0
# 打开图像
pixels = image.load()
# 获取图像的宽度和高度
width, height = image.size
############################开始对边框的内部节点进行处理################################
# 获取目标像素点的颜色
target_color = pixels[x, y]
# 循环遍历图像中的每个像素点
for i in range(x_1, x_2):
for j in range(y_1, y_2):
# 如果像素颜色与目标颜色相同，则替换为周围区域颜色的平均值
if my_similar(pixels[i, j], target_color):
# 计算周围水平和垂直区域的颜色平均值
neighborhood_x = []
neighborhood_y = []
for m in range(i - redius, i + radius + 1):
if 0 <= m < width:
neighborhood_x.append(pixels[m, j])
for n in range(j - redius, j + radius + 1):
if 0 <= n < height:
neighborhood_y.append(pixels[i, n])
neighborhood, direction = get_right_neighborhood(target_color, neighborhood_x, neighborhood_y)
neighborhood = [n for n in neighborhood if not my_similar(n, target_color)]
neighborhood = np.array(neighborhood)
average_color = tuple(np.mean(neighborhood, axis=0, dtype=int))
average_color_part_1 = tuple(np.mean(neighborhood[0:int(len(neighborhood) / 2)], axis=0, dtype=int))
average_color_part_2 = tuple(np.mean(neighborhood[int(len(neighborhood) / 2) + 1:], axis=0, dtype=int))
# 替换像素颜色
pixels[i, j] = average_color
# 如果是水平方向
if direction == 1:
for m in range(i - int(redius_border / 3), i):
if 0 <= m < width:
pixels[m, j] = average_color_part_1
for m in range(i, i + int(redius_border / 3) + 1):
if 0 <= m < width:
pixels[m, j] = average_color_part_2
# 如果是垂直方向
if direction == 2:
for n in range(j - int(redius_border / 3), j):
if 0 <= n < height:
pixels[i, n] = average_color_part_1
for n in range(j, j + int(redius_border / 3) + 1):
if 0 <= n < height:
pixels[i, n] = average_color_part_2
step += 1
if step % step_add == 0 and step < step_max:
normal_point, unnormal_point = get_outlier(neighborhood, model)
unnormal_point = [tuple(x) for x in unnormal_point]
all_outliers.extend(unnormal_point)
############################开始对边框的边缘节点进行处理################################
# 使用Counter来统计三元组的出现次数
all_outlier_counts = Counter(all_outliers)
# 获取出现次数最多的十个三元组，用来去边框
top_outliers = all_outlier_counts.most_common(top_outlier_num)
# 循环遍历图像中的每个像素点
for i in range(x_1, x_2):
for j in range(y_1, y_2):
# 如果该点和异常点中的颜色相似，就进行替换
if my_similar_border(pixels[i, j], top_outliers):
# 计算周围水平和垂直区域的颜色平均值
neighborhood_x = []
neighborhood_y = []
for m in range(i - int(radius / 3), i + int(radius / 3) + 1):
if 0 <= m < width:
neighborhood_x.append(pixels[m, j])
for n in range(j - int(radius / 3), j + int(radius / 3) + 1):
if 0 <= n < height:
neighborhood_y.append(pixels[i, n])
neighborhood, direction = get_right_neighborhood(target_color, neighborhood_x, neighborhood_y)
neighborhood = [n for n in neighborhood if not n in top_outliers]
neighborhood = np.array(neighborhood)
average_color = tuple(np.mean(neighborhood, axis=0, dtype=int))
# 替换像素颜色
pixels[i, j] = average_color
return image个人运行代码如下：
其中 threhold 参数自己使用的时候为 1 ，效果还可以。 redius 必须为 3 的倍数，redius_border 同上。保存图像时需要先进行.convert('RGB')操作。
if __name__ == '__main__':
# 判断rgb相似度用，可以小一些
threhold = 1
threhold_border = 3
# 采样半径，可以设的小一些，可以根据边框的粗细来设定
redius = 9
redius_border = 9
# 对边框进行处理需要调整的参数
step_add = 37
step_max = 2000
step = 0
all_outliers = []
top_outlier_num = 10
# 创建Isolation Forest模型
model = IsolationForest(contamination=0.05) # 设置异常点的比例
image_file = r"" # 替换为您的输入图像路径
for jpg in tqdm(os.listdir(image_file)):
if ".jpg" in jpg:
jpg_path = os.path.join(image_file, jpg) # 图片路径
image = Image.open(jpg_path) # 读取图片
# 获取框位置(代码)
imag=cv_imread(jpg_path)
labels=get_coordinate(imag)
for i in labels:
x_1, y_1, x_2, y_2 = i
x, y = x_1, y_1
try:
image = replace_color_around_point(image, x, y, x_1, y_1, x_2, y_2, redius)
except:
image=image
print(jpg)
image.show()
# image = image.convert('RGB').save(jpg_path)最后附上连续代码，以后万一又用上了呢（笑）
from PIL import Image
import numpy as np
from sklearn.ensemble import IsolationForest
from collections import Counter
import cv2, os
from tqdm import tqdm
def cv_imread(filePath):
cv_img = cv2.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)
## imdecode读取的是rgb，如果后续需要opencv处理的话，需要转换成bgr，转换后图片颜色会变化
##cv_img=cv2.cvtColor(cv_img,cv2.COLOR_RGB2BGR)
return cv_img
def get_coordinate(img):
# 转换为HSV颜色空间
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# print(hsv)
# 定义不同颜色的HSV值范围
color1_lower = np.array([30, 80, 255])
color1_upper = np.array([40, 200, 255])
# 在HSV图像上应用颜色范围掩码
mask1 = cv2.inRange(hsv, color1_lower, color1_upper)
# 对原始图像和掩码进行位运算
result1 = cv2.bitwise_and(img, img, mask=mask1)
# 查找矩形框的轮廓
contours1, hierarchy1 = cv2.findContours(mask1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 获取轮廓对应坐标
labels = []
for contour1 in contours1:
x, y, w, h = cv2.boundingRect(contour1)
labels.append((x, y, x + w, y + h))
return labels
# 判断颜色是否相似
def my_similar(a, b):
if sum([abs(x - y) for (x, y) in zip(list(a), list(b))]) / 3 < threhold:
return True
return False
# 判断颜色是否相似
def my_similar_border(a, b_s):
for b in b_s:
if sum([abs(x - y) for (x, y) in zip(list(a), list(b)[0])]) / 3 < threhold_border:
return True
return False
# 获取异常值，用于处理边框的边缘Q
def get_outlier(tmp, model):
normal_point = []
unnormal_point = []
# 将数据传递给模型进行训练
model.fit(tmp)
# 获取每个数据点的预测标签，-1 表示异常点，1 表示正常点
labels = model.predict(tmp)
# 打印每个数据点的标签
for i, label in enumerate(labels):
if label == -1:
unnormal_point.append(tmp[i])
if label == 1:
normal_point.append(tmp[i])
return normal_point, unnormal_point
# 在水平或处置方向上获取邻居节点，以便进行颜色替换
def get_right_neighborhood(target_color, neighborhood_x, neighborhood_y):
diff_x = 0
diff_y = 0
for neighborhood in neighborhood_x:
diff_x += abs(sum([x - y for x, y in zip(list(neighborhood), list(target_color))]) / 3)
for neighborhood in neighborhood_y:
diff_y += abs(sum([x - y for x, y in zip(list(neighborhood), list(target_color))]) / 3)
if diff_x > diff_y:
return neighborhood_x, 1
return neighborhood_y, 2
def replace_color_around_point(image, x, y, x_1, y_1, x_2, y_2, radius):
# 采样步数
step = 0
# 打开图像
pixels = image.load()
# 获取图像的宽度和高度
width, height = image.size
############################开始对边框的内部节点进行处理################################
# 获取目标像素点的颜色
target_color = pixels[x, y]
# 循环遍历图像中的每个像素点
for i in range(x_1, x_2):
for j in range(y_1, y_2):
# 如果像素颜色与目标颜色相同，则替换为周围区域颜色的平均值
if my_similar(pixels[i, j], target_color):
# 计算周围水平和垂直区域的颜色平均值
neighborhood_x = []
neighborhood_y = []
for m in range(i - redius, i + radius + 1):
if 0 <= m < width:
neighborhood_x.append(pixels[m, j])
for n in range(j - redius, j + radius + 1):
if 0 <= n < height:
neighborhood_y.append(pixels[i, n])
neighborhood, direction = get_right_neighborhood(target_color, neighborhood_x, neighborhood_y)
neighborhood = [n for n in neighborhood if not my_similar(n, target_color)]
neighborhood = np.array(neighborhood)
average_color = tuple(np.mean(neighborhood, axis=0, dtype=int))
average_color_part_1 = tuple(np.mean(neighborhood[0:int(len(neighborhood) / 2)], axis=0, dtype=int))
average_color_part_2 = tuple(np.mean(neighborhood[int(len(neighborhood) / 2) + 1:], axis=0, dtype=int))
# 替换像素颜色
pixels[i, j] = average_color
# 如果是水平方向
if direction == 1:
for m in range(i - int(redius_border / 3), i):
if 0 <= m < width:
pixels[m, j] = average_color_part_1
for m in range(i, i + int(redius_border / 3) + 1):
if 0 <= m < width:
pixels[m, j] = average_color_part_2
# 如果是垂直方向
if direction == 2:
for n in range(j - int(redius_border / 3), j):
if 0 <= n < height:
pixels[i, n] = average_color_part_1
for n in range(j, j + int(redius_border / 3) + 1):
if 0 <= n < height:
pixels[i, n] = average_color_part_2
step += 1
if step % step_add == 0 and step < step_max:
normal_point, unnormal_point = get_outlier(neighborhood, model)
unnormal_point = [tuple(x) for x in unnormal_point]
all_outliers.extend(unnormal_point)
############################开始对边框的边缘节点进行处理################################
# 使用Counter来统计三元组的出现次数
all_outlier_counts = Counter(all_outliers)
# 获取出现次数最多的十个三元组，用来去边框
top_outliers = all_outlier_counts.most_common(top_outlier_num)
# 循环遍历图像中的每个像素点
for i in range(x_1, x_2):
for j in range(y_1, y_2):
# 如果该点和异常点中的颜色相似，就进行替换
if my_similar_border(pixels[i, j], top_outliers):
# 计算周围水平和垂直区域的颜色平均值
neighborhood_x = []
neighborhood_y = []
for m in range(i - int(radius / 3), i + int(radius / 3) + 1):
if 0 <= m < width:
neighborhood_x.append(pixels[m, j])
for n in range(j - int(radius / 3), j + int(radius / 3) + 1):
if 0 <= n < height:
neighborhood_y.append(pixels[i, n])
neighborhood, direction = get_right_neighborhood(target_color, neighborhood_x, neighborhood_y)
neighborhood = [n for n in neighborhood if not n in top_outliers]
neighborhood = np.array(neighborhood)
average_color = tuple(np.mean(neighborhood, axis=0, dtype=int))
# 替换像素颜色
pixels[i, j] = average_color
return image
if __name__ == '__main__':
# 判断rgb相似度用，可以小一些
threhold = 1
threhold_border = 3
# 采样半径，可以设的小一些，可以根据边框的粗细来设定
redius = 9
redius_border = 9
# 对边框进行处理需要调整的参数
step_add = 37
step_max = 2000
step = 0
all_outliers = []
top_outlier_num = 10
# 创建Isolation Forest模型
model = IsolationForest(contamination=0.05) # 设置异常点的比例
image_file = r"" # 替换为您的输入图像路径
for jpg in tqdm(os.listdir(image_file)):
if ".jpg" in jpg:
jpg_path = os.path.join(image_file, jpg) # 图片路径
image = Image.open(jpg_path) # 读取图片
# 获取框位置(代码)
imag=cv_imread(jpg_path)
labels=get_coordinate(imag)
for i in labels:
x_1, y_1, x_2, y_2 = i
x, y = x_1, y_1
try:
image = replace_color_around_point(image, x, y, x_1, y_1, x_2, y_2, redius)
except:
image=image
print(jpg)
image.show()
# image = image.convert('RGB').save(jpg_path)本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
