def run_predict(self): #load the trained model from .hdf5 file model = load_model( r"C:\Users\10536\Desktop\MSc_Project\py\Unet\my_test\my_unet.hdf5") model.summary() pack = np.zeros((self.frames, self.col, self.row, 1)) #model.predict requires the input in a format of [frames,size,size,1] for i in range(self.frames): #resize each frame to 512*512 img_h = figure_processer.hist_eq(self.fin_sq[:, :, i] * 255) #enhance the picture pack[i, :, :, 0] = img_h / (255) pre_y = model.predict(pack, batch_size=1, verbose=1) return pre_y
def save_data(file_name,path): fin = read_bin.read_bin2(file_name,path) row = fin.shape[0] col = fin.shape[1] frames = fin.shape[2] for i in range(frames): label = fin[:,:,i] img_h = figure_processer.hist_eq(label*256) pic_new = image.new("L", (row, col)) for j in range(row): for k in range(col): pic_new.putpixel((j,k), int((img_h[j][k]+1))) new_img = pic_new.resize((512,512),image.BILINEAR) new_img = new_img.transpose(image.ROTATE_270) new_img = new_img.transpose(image.FLIP_LEFT_RIGHT) new_img.save(r"C:\Users\10536\Desktop\MSc_Project\py\data_set\%s__%d.jpg"%(file_name,i), "JPEG") print("save %d"%(i))
def mean_pro(file_name, path): fin = read_bin.read_bin2(file_name, path) scan70 = fin[:, 70, :].astype(np.float32) img_h = figure_processer.hist_eq(scan70 * 255) img_gamma = figure_processer.gamma(img_h, 0.8) img_bright = figure_processer.bright(img_gamma, 2.5, 0 / 255) img_m = figure_processer.m_filter(img_bright.astype(np.float32)) img_hp = figure_processer.highpass_filter(img_m * 255) / 255 rate = figure_processer.get_rate1(img_hp) np.savetxt(r"%s_rate_1.txt" % (path), rate.astype(np.int), fmt='%d') rate_f = rate_processer.rate_proc(rate) np.savetxt(r"%s_rate_f.txt" % (path), rate_f.astype(np.int), fmt='%d') rate_2 = Kmeans_pro.Kmeans_pro(img_h, 5) plt.figure() plt.title('%s' % (file_name)) plt.imshow(rate_2) plt.show() rate2 = rate_processer_2.get_rate2(rate_2) np.savetxt(r"%s_rate_2.txt" % (path), rate2.astype(np.int), fmt='%d') print("%s done" % (file_name)) return rate, rate_f, rate2, img_h
def save_data_masked(file_name,path): fin = read_bin.read_bin2(file_name,path) row = fin.shape[0] col = fin.shape[1] frames = fin.shape[2] z = 0 sel = np.linspace(120,frames-140,5) sel = sel.astype(np.int) for i in sel: z = z+1 label = fin[:,:,i] img_h = figure_processer.hist_eq(label*256) img_mask = Kmeans_pro.Kmeans_pro(img_h,3) img_m = figure_processer.m_filter(img_mask.astype(np.float32)) pic_new = image.new("L", (row, col)) for j in range(row): for k in range(col): pic_new.putpixel((j,k), int((img_m[j][k]*255/3+1))) new_img = pic_new.resize((512,512),image.BILINEAR) new_img = new_img.transpose(image.ROTATE_270) new_img = new_img.transpose(image.FLIP_LEFT_RIGHT) new_img.save(r"C:\Users\10536\Desktop\MSc_Project\py\data_Kmeans\%s__%d_mask.jpg"%(file_name,z), "JPEG") print("save %d"%(i))
import numpy as np import read_bin import figure_processer import rate_processer import Kmeans_pro import rate_processer_2 import matplotlib.pyplot as plt import get_file path = r"C:\Users\10536\Desktop\MSc_Project\files\example_dia_motion_B" file_name = "example_dia_motion_B" fin = read_bin.read_bin2(file_name, path) scan70 = fin[:, 45, :].astype(np.float32) img_crop = figure_processer.crop(scan70) img_h = figure_processer.hist_eq(img_crop * 255) ####img_gamma = figure_processer.gamma(img_h,1.2) ####img_bright = figure_processer.bright(img_gamma,1.5,0/255) ####img_m = figure_processer.m_filter(img_bright.astype(np.float32)) ####img_hp = figure_processer.highpass_filter(img_m*255)/255 ####rate = figure_processer.get_rate1(img_hp) ####rate_m = figure_processer.sigma(rate) ####np.savetxt(r"%s_rate_1.txt"%(path),rate_m.astype(np.int),fmt='%d') #rate_1 = figure_processer.get_rate1(img_gamma) rate_1_1 = figure_processer.get_rate1(img_h) rate_1_1_m = figure_processer.sigma(rate_1_1) #return the cropped value back rate_1_1_m = rate_1_1_m + 0.4 * scan70.shape[0] np.savetxt(r"%s_rate_2.txt" % (path), rate_1_1_m.astype(np.int), fmt='%d') rate_1_2 = figure_processer.get_rate1(img_crop)