def test(file_name, threshold):
#读取测试文件地址
path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\10-binary_file'
#读取统计结果各小时矩阵
label_path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\result'
f = open(os.path.join(path, file_name),'r')
s = f.read()
paraDict,matrix = cleanUpPic.resolveRadar(s)
xsize = paraDict['xsize']
ysize = paraDict['ysize']
img_origin = np.reshape(matrix, (ysize, xsize))
hour = file_name[-4:-2]
label_data = np.loadtxt(os.path.join(label_path,'final_matrix_'+hour))
img_bool = np.zeros((ysize, xsize))
for i in xrange(ysize):
for j in xrange(xsize):
dzb = img_origin[i][j]
if dzb!=-1:
label_test_data = np.reshape(label_data[dzb], (421,561))
if float(label_test_data[i][j]) < threshold:
img_bool[i][j] = 1
final_image = img_origin * img_bool
plt.subplot(2,1,1)
plt.imshow(img_origin)
plt.subplot(2,1,2)
plt.imshow(final_image)
plt.show()
def statistics_history_distribution():
#读取历史数据的地址
path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\10-binary_file\test'
#保存结果的地址
save_path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\result'
y_size = 421
x_size = 561
total_matrix = np.zeros((24,16,421,561), dtype= np.int)
#各小时数据和统计
count = np.zeros(24)
file_list=os.listdir(path)
for file_item in file_list:
f = open(os.path.join(path, file_item),'r')
s = f.read()
paraDict,matrix = cleanUpPic.resolveRadar(s)
xsize = paraDict['xsize']
ysize = paraDict['ysize']
img_origin = np.reshape(matrix, (ysize, xsize))
hour = int(file_item[-4:-2])
count[hour] +=1
for i in xrange(16):
total_matrix[hour][i] += (img_origin ==i)*1
#每个小时的矩阵转成2维发送
for i in xrange(24):
final_matrix = total_matrix[i]*(1.0/count[i])
final_2d_matrix = np.reshape(final_matrix, (16, ysize*xsize))
np.savetxt(os.path.join(save_path,"final_matrix_"+str(i)), final_2d_matrix, fmt = '%1.7f')
#total总和矩阵转成二维发送
total_2d_matrix = np.reshape(total_matrix, (24*16, ysize*xsize))
np.savetxt(os.path.join(save_path,"total_matrix"),total_2d_matrix, fmt = '%d')
return total_matrix
#list all of the documents
list_doc = []
for item in os.listdir(savePath):
list_doc.append(item)
list_doc.sort()
count = 0
#print list_doc
for item in list_doc:
#read the image
f=open(os.path.join(savePath,item),'r')
s= f.read()
#resolve it read the whole image
paraDict,matrix=cleanUpPic.resolveRadar(s)
xsize=paraDict['xsize']
ysize=paraDict['ysize']
matrix=np.reshape(matrix,(ysize,xsize))
print xsize, ysize
normal_matrix.append(matrix)
times_list.append(Time_Trans(paraDict['timeStr']))
#prepic cleanup the noise of the image
pic=cleanUpPic.PrePic()
pic.Load_Pic_Matrix(matrix)
pic.calc_from_DB()
bool_matrix.append(pic.pic_bool)
all_matrix.append(pic.pic_radar)
