vis_dstl.py

# def predict_id(id, model, trs):
#     img = utils.M(id)
#     x = utils.stretch_n(img)

#     cnv = np.zeros((960, 960, 8)).astype(np.float32)
#     prd = np.zeros((n_classes, 960, 960)).astype(np.float32)
#     cnv[:img.shape[0], :img.shape[1], :] = x

#     for i in range(0, 6):
#         line = []
#         for j in range(0, 6):
#             line.append(cnv[i * ISZ:(i + 1) * ISZ, j * ISZ:(j + 1) * ISZ])

#         x = 2 * np.transpose(line, (0, 3, 1, 2)) - 1
#         x = np.transpose(x, (0,2,3,1))
#         tmp = model.predict(x, batch_size=4)
#         for j in range(tmp.shape[0]):
#             prd[:, i * ISZ:(i + 1) * ISZ, j * ISZ:(j + 1) * ISZ] = tmp[j]

#     # trs = [0.4, 0.1, 0.4, 0.3, 0.3, 0.5, 0.3, 0.6, 0.1, 0.1]
#     for i in range(n_classes):
#         prd[i] = prd[i] > trs[i]

#     return prd[:, :img.shape[0], :img.shape[1]]


# def check_predict(id='6120_2_3'):
#     params = {'input_shape':(n_channels, img_rows, img_cols), 
#             'n_classes':n_classes,
#             'feat_scale':feat_scale} 
#     model = unet(params)
#     model.load_weights('./savedModel/myModel.hdf5')
#     model.compile(optimizer=Adam(),
#                   loss='binary_crossentropy',
#                   metrics=[jaccard_coef, jaccard_coef_int, 'accuracy'])
                  
#     msk = predict_id(id, model, [0.4, 0.1, 0.4, 0.3, 0.3, 0.5, 0.3, 0.6, 0.1, 0.1])
#     img = M(id)

#     plt.figure()
#     ax1 = plt.subplot(121)
#     ax1.set_title('image ID:6120_2_3')
#     ax1.imshow(img[:, :, 5], cmap=plt.get_cmap('gist_ncar'))
#     ax2 = plt.subplot(122)
#     ax2.set_title('predict bldg pixels')
#     ax2.imshow(msk[0], cmap=plt.get_cmap('gray'))
#     plt.show()
    
    # ax3 = plt.subplot(133)
    # ax3.set_title('predict bldg polygones')
    # ax3.imshow(mask_for_polygons(mask_to_polygons(msk[0], epsilon=1), img.shape[:2]), cmap=plt.get_cmap('gray'))

    

############  test

# params = {'input_shape':(n_channels, img_rows, img_cols), 
#         'n_classes':n_classes,
#         'feat_scale':feat_scale} 
# model = unet(params)
# model.load_weights('./savedModel/myModel.hdf5')
# model.compile(optimizer=Adam(),
#                 loss='binary_crossentropy',
#                 metrics=[jaccard_coef, jaccard_coef_int, 'accuracy'])

# id='6120_2_3'
# trs = [0.4, 0.1, 0.4, 0.3, 0.3, 0.5, 0.3, 0.6, 0.1, 0.1]      

# img = np.load('results/img.npy')
# x = np.load('results/x.npy')

# cnv = np.zeros((960, 960, 8)).astype(np.float32)
# prd = np.zeros((n_classes, 960, 960)).astype(np.float32)
# cnv[:img.shape[0], :img.shape[1], :] = x

# for i in range(0, 6):
#     line = []
#     for j in range(0, 6):
#         line.append(cnv[i * ISZ:(i + 1) * ISZ, j * ISZ:(j + 1) * ISZ])

#     x = 2 * np.transpose(line, (0, 3, 1, 2)) - 1
#     x = np.transpose(x, (0,2,3,1))
#     tmp = model.predict(x, batch_size=4)
#     for j in range(tmp.shape[0]):
#         prd[:, i * ISZ:(i + 1) * ISZ, j * ISZ:(j + 1) * ISZ] = tmp[j]

# # trs = [0.4, 0.1, 0.4, 0.3, 0.3, 0.5, 0.3, 0.6, 0.1, 0.1]
# for i in range(n_classes):
#     prd[i] = prd[i] > trs[i]

# msk =  prd[:, :img.shape[0], :img.shape[1]]



# # img = M(id)

# plt.figure()
# ax1 = plt.subplot(121)
# ax1.set_title('image ID:6120_2_3')
# ax1.imshow(img[:, :, 5], cmap=plt.get_cmap('gist_ncar'))
# ax2 = plt.subplot(122)
# ax2.set_title('predict bldg pixels')
# ax2.imshow(msk[0], cmap=plt.get_cmap('gray'))
# plt.show()