fixed bugs mentioned in update-0503.xlsx
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5c2e6f4e73
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fde841c037
@ -125,16 +125,24 @@ class BasicAICD(AlgFrontend):
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block_size = (xsize, cell_size[1])
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if block_xy[1] + block_size[1] > ysize:
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block_size = (xsize, ysize - block_xy[1])
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np.seterr(divide='ignore',invalid='ignore')
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block_data = temp_in_ds.ReadAsArray(*block_xy, *block_size)
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max_diff=block_data.max()
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min_diff=block_data.min()
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block_data = (block_data - min_diff) / (max_diff - min_diff) * 255
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block_data = block_data.astype(np.uint8)
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out_normal_ds.GetRasterBand(1).WriteArray(block_data, *block_xy)
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# os.system('pause')
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# hist_t, _ = np.histogram(block_data, bins=256, range=(0, 256))
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# hist += hist_t
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# print(hist)
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del temp_in_ds
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del out_normal_ds
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try:
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# os.system('pause')
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os.remove(out_tif)
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except:
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pass
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@ -144,8 +144,9 @@ class EvaluationPlugin(BasicPlugin):
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for k in range(2):
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for l in range(2):
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cfm[k,l] += np.sum((pred_block == k) & (gt_block >= l))
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# cfm[k,l] += np.sum((pred_block == k) & (gt_block >= l))
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cfm[k,l] += np.sum((pred_block == k) & (gt_block == l))
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result_path = os.path.join(Project().other_path, f'{layer.name}_{os.path.basename(gt)}_evaluation.txt')
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with open(result_path, 'w', encoding='utf-8') as f:
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f.write(f'预测结果：{layer.path}\n')
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@ -169,7 +170,7 @@ class EvaluationPlugin(BasicPlugin):
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Kappa: {kappa(cfm)}
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''')
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f.flush()
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# os.system('pause')
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txt=f'''
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预测结果：{layer.path}\n
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@ -193,12 +194,13 @@ class EvaluationPlugin(BasicPlugin):
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F1: {cfm[1,1] / (np.sum(cfm[1,:]) + np.sum(cfm[:,1]) - cfm[1,1])}
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Kappa: {kappa(cfm)}
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'''
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# self.msgbox = Win(txt)
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# self.msgbox.show()
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self.msgbox = Win(txt)
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self.msgbox.show()
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# os.system(f'c:/windows/notepad.exe "{result_path}"')
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# self.send_message.emit('精度评估完成')
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self.send_message.emit('精度评估完成,结果保存在'+result_path)
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def show_dialog(self):
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dialog = EvalutationDialog(self.mainwindow)
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@ -212,10 +214,13 @@ class EvaluationPlugin(BasicPlugin):
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t.start()
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from .window import Ui_Form
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class Win(QWidget,Ui_Form):
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# class Win(QWidget,Ui_Form):
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# def __init__(self,txt):
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# super().__init__()
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# self.setupUi(self)
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# self.label.setText(txt)
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def __init__(self,txt):
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super().__init__()
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self.setupUi(self)
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self.label.setText(txt)
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@ -138,7 +138,7 @@ class ExportPlugin(BasicPlugin):
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[1, 255, 0, 0]
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]
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if self.style_path is '':
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if self.style_path == '':
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style = default_style
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else:
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style = np.loadtxt(self.style_path, comments='#', delimiter=',')
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@ -160,27 +160,42 @@ class ExportPlugin(BasicPlugin):
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# TODO: check data range is 0-1
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def get_color(v):
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first_color = []
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second_color = []
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first_value = 0
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second_value = 1
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for s in style:
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if s[0] <= v:
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first_value = s[0]
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first_color = s[1:]
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else:
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second_value = s[0]
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second_color = s[1:]
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break
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if second_value == 1:
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return np.array(style[-1][1:])
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# first_color = []
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# second_color = []
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# first_value = 0
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# second_value = 1
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# for s in style:
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# if s[0] <= v:
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# first_value = s[0]
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# first_color = s[1:]
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# else:
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# second_value = s[0]
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# second_color = s[1:]
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# break
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# if second_value == 1:
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# return np.array(style[-1][1:])
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# first_dis = (v - first_value) / (second_value - first_value)
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# second_dis = (second_value - v) / (second_value - first_value)
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# first_color = np.array(first_color)
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# second_color = np.array(second_color)
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# color = first_color* first_dis + second_color * second_dis
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# return np.floor(color)
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if v==0:
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return np.array(style[-1][1:])
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elif v==1:
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return np.array(style[-2][1:])
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else:
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first_color =style[-1][1:]
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second_color=style[-2][1:]
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first_color = np.array(first_color)
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second_color=np.array(second_color)
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color_inter=first_color*(1-v)+second_color*v
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return color_inter
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first_dis = (v - first_value) / (second_value - first_value)
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second_dis = (second_value - v) / (second_value - first_value)
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first_color = np.array(first_color)
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second_color = np.array(second_color)
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color = first_color* first_dis + second_color * second_dis
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return np.floor(color)
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get_color = np.frompyfunc(get_color, nin=1, nout=1)
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@ -13,46 +13,37 @@ from datetime import datetime
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import numpy as np
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import torch
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import torch.nn.functional as F
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def adaptiveMedianDeNoise(count, original):
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# 初始窗口大小
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startWindow = 3
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# 卷积范围
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c = count // 2
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rows, cols = original.shape
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newI = np.zeros(original.shape)
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# median =
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for i in range(c, rows - c):
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for j in range(c, cols - c):
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startWindow = 3
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k = int(startWindow / 2)
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median = np.median(original[i - k:i + k + 1, j - k:j + k + 1])
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mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
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ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
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if mi < median < ma:
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if mi < original[i, j] < ma:
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newI[i, j] = original[i, j]
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else:
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newI[i, j] = median
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else:
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while True:
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startWindow = startWindow + 2
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k = int(startWindow / 2)
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median = np.median(original[i - k:i + k + 1, j - k:j + k + 1])
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mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
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ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
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if mi < median < ma or startWindow >= count:
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break
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if mi < median < ma or startWindow > count:
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if mi < original[i, j] < ma:
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newI[i, j] = original[i, j]
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import cv2
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from tqdm import tqdm
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def adaptiveMedianDeNoise(count, image):
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origen = 3 # 初始窗口大小
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board = origen//2 # 初始应扩充的边界
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# max_board = max_size//2 # 最大可扩充的边界
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copy = cv2.copyMakeBorder(image, *[board]*4, borderType=cv2.BORDER_DEFAULT) # 扩充边界
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out_img = np.zeros(image.shape)
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for i in tqdm(range(image.shape[0])):
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for j in range(image.shape[1]):
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def sub_func(src, size): # 两个层次的子函数
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kernel = src[i:i+size, j:j+size]
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# print(kernel)
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z_med = np.median(kernel)
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z_max = np.max(kernel)
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z_min = np.min(kernel)
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if z_min < z_med < z_max: # 层次A
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if z_min < image[i][j] < z_max: # 层次B
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return image[i][j]
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else:
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newI[i, j] = median
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return z_med
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else:
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next_size = cv2.copyMakeBorder(src, *[1]*4, borderType=cv2.BORDER_DEFAULT) # 增尺寸
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size = size+2 # 奇数的核找中值才准确
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if size <= count:
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return sub_func(next_size, size) # 重复层次A
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else:
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return z_med
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out_img[i][j] = sub_func(copy, origen)
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return newI
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return out_img
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@FILTER.register
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class AdaptiveFilter(AlgFrontend):
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@ -13,45 +13,38 @@ from datetime import datetime
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import numpy as np
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import torch
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import torch.nn.functional as F
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def adaptiveMedianDeNoise(count, original):
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# 初始窗口大小
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startWindow = 3
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# 卷积范围
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c = count // 2
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rows, cols = original.shape
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newI = np.zeros(original.shape)
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# median =
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for i in range(c, rows - c):
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for j in range(c, cols - c):
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k = int(startWindow / 2)
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median = np.median(original[i - k:i + k + 1, j - k:j + k + 1])
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mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
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ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
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if mi < median < ma:
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if mi < original[i, j] < ma:
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newI[i, j] = original[i, j]
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else:
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newI[i, j] = median
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else:
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while True:
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startWindow = startWindow + 2
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k = int(startWindow / 2)
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median = np.median(original[i - k:i + k + 1, j - k:j + k + 1])
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mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
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ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
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if mi < median < ma or startWindow >= count:
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break
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if mi < median < ma or startWindow >= count:
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if mi < original[i, j] < ma:
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newI[i, j] = original[i, j]
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import pandas as pd
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import cv2
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from tqdm import tqdm
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def adaptiveMedianDeNoise(count, image):
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origen = 3 # 初始窗口大小
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board = origen//2 # 初始应扩充的边界
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# max_board = max_size//2 # 最大可扩充的边界
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copy = cv2.copyMakeBorder(image, *[board]*4, borderType=cv2.BORDER_DEFAULT) # 扩充边界
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out_img = np.zeros(image.shape)
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for i in tqdm(range(image.shape[0])):
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for j in range(image.shape[1]):
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def sub_func(src, size): # 两个层次的子函数
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kernel = src[i:i+size, j:j+size]
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# print(kernel)
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z_med = np.median(kernel)
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z_max = np.max(kernel)
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z_min = np.min(kernel)
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if z_min < z_med < z_max: # 层次A
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if z_min < image[i][j] < z_max: # 层次B
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return image[i][j]
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else:
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newI[i, j] = median
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return z_med
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else:
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next_size = cv2.copyMakeBorder(src, *[1]*4, borderType=cv2.BORDER_DEFAULT) # 增尺寸
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size = size+2 # 奇数的核找中值才准确
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if size <= count:
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return sub_func(next_size, size) # 重复层次A
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else:
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return z_med
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out_img[i][j] = sub_func(copy, origen)
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return newI
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return out_img
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@FILTER.register
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class AdaptiveNPFilter(AlgFrontend):
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@ -138,7 +138,7 @@ class OTSUAlg(AlgFrontend):
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band = ds.GetRasterBand(1)
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# band_count = ds.RasterCount
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hist = np.zeros(256, dtype=np.int)
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hist = np.zeros(256, dtype=np.int_)
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xsize = ds.RasterXSize
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ysize = ds.RasterYSize
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