def predict_multiclass(img_file, out_path):
print("[INFO] opening image...")
input_img = load_img_by_gdal(img_file)
if im_type == UINT8:
input_img = input_img / 255.0
elif im_type == UINT10:
input_img = input_img / 1024.0
elif im_type == UINT16:
input_img = input_img / 65535.0
input_img = np.clip(input_img, 0.0, 1.0)
"""checke model file"""
print("model file: {}".format(model_file))
if not os.path.isfile(model_file):
print("model does not exist:{}".format(model_file))
sys.exit(-2)
model = load_model(model_file)
abs_filename = os.path.split(img_file)[1]
abs_filename = abs_filename.split(".")[0]
print(abs_filename)
if FLAG_APPROACH_PREDICT == 0:
print("[INFO] predict image by orignal approach\n")
result = orignal_predict_onehot(input_img, im_bands, model,
window_size)
output_file = ''.join(
[out_path, '/original_predict_', abs_filename, '_multiclass.png'])
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result * 128)
elif FLAG_APPROACH_PREDICT == 1:
print("[INFO] predict image by smooth approach\n")
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=target_class, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_multiclass,
PLOT_PROGRESS=False)
for b in range(target_class):
output_file = ''.join([
out_path, '/mask_multiclass_', abs_filename, '_',
dict_target[b], '.png'
])
cv2.imwrite(output_file, result[:, :, b])
print("Saved to: {}".format(output_file))
gc.collect()
def predict_binary_jaccard(img_file, output_file):
print("[INFO] opening image...")
input_img = load_img_by_gdal(img_file)
if im_type == UINT8:
input_img = input_img / 255.0
elif im_type == UINT10:
input_img = input_img / 1024.0
elif im_type == UINT16:
input_img = input_img / 65535.0
input_img = np.clip(input_img, 0.0, 1.0)
input_img = input_img.astype(np.float16)
model = load_model(model_file)
if FLAG_APPROACH_PREDICT==0:
print("[INFO] predict image by orignal approach\n")
result = orignal_predict_notonehot(input_img,im_bands, model, window_size)
abs_filename = os.path.split(img_file)[1]
abs_filename = abs_filename.split(".")[0]
# output_file = ''.join([output_path, '/original_pred_',
# abs_filename, '_', dict_target[FLAG_TARGET_CLASS],'_jaccard.png'])
output_file = ''.join([output_path, '/mask_binary_',
abs_filename, '_', dict_target[FLAG_TARGET_CLASS], '_jaccard_original.png'])
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result*128)
elif FLAG_APPROACH_PREDICT==1:
print("[INFO] predict image by smooth approach\n")
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=target_class, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_binary_notonehot,
PLOT_PROGRESS=False
)
cv2.imwrite(output_file, result)
print("Saved to: {}".format(output_file))
gc.collect()
result = orignal_predict_notonehot(input_img, im_bands, model,
window_size)
output_file = ''.join([
'../../data/predict/', dict_network[FLAG_USING_NETWORK],
'/sat_4bands/original_pred_', abs_filename, '_',
dict_target[FLAG_TARGET_CLASS], '_onlyjaccard.png'
])
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result * 128)
elif FLAG_APPROACH_PREDICT == 1:
print("[INFO] predict image by smooth approach\n")
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=target_class, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_binary_notonehot)
# output_file = ''.join(['../../data/predict/', dict_network[FLAG_USING_NETWORK],'/sat_4bands/mask_binary_',
# abs_filename, '_', dict_target[FLAG_TARGET_CLASS],'_onlyjaccard.png'])
output_file = ''.join([
'../../data/test/paper/pred/mask_binary_', abs_filename, '_',
dict_target[FLAG_TARGET_CLASS], '_onlyjaccard.png'
])
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result)
gc.collect()
def predict_binary_for_single_image(input_dict={}):
gup_id = input_dict['GPUID']
os.environ["CUDA_VISIBLE_DEVICES"] = gup_id
window_size = input_dict['windsize']
im_bands = input_dict['im_bands']
im_type = input_dict['dtype']
FLAG_APPROACH_PREDICT = input_dict['strategy']
img_file = input_dict['image_file']
model_file = input_dict['model_file']
output_file = input_dict['mask_path']
out_bands = 1
FLAG_ONEHOT = 0
if input_dict['onehot']:
FLAG_ONEHOT = 1
input_img = load_img_by_gdal(img_file)
if im_type == UINT8:
input_img = input_img / 255.0
elif im_type == UINT10:
input_img = input_img / 1024.0
elif im_type == UINT16:
input_img = input_img / 65535.0
input_img = np.clip(input_img, 0.0, 1.0)
input_img = input_img.astype(np.float16) # test accuracy
"""checke model file"""
print("model file: {}".format(model_file))
if not os.path.isfile(model_file):
print("model does not exist:{}".format(model_file))
sys.exit(-2)
model = load_model(model_file)
if FLAG_APPROACH_PREDICT==0:
print("[INFO] predict image by orignal approach\n")
if FLAG_ONEHOT:
result = orignal_predict_onehot(input_img, im_bands, model, window_size)
else:
result = orignal_predict_notonehot(input_img,im_bands, model, window_size)
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result*128)
elif FLAG_APPROACH_PREDICT==1:
print("[INFO] predict image by smooth approach\n")
if FLAG_ONEHOT:
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=out_bands, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_binary_onehot
)
else:
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=out_bands, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_binary_notonehot
)
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result)
print("Saved to {}".format(output_file))
gc.collect()
return 0
def predict_multiclass_for_batch_image(input_dict={}):
gup_id = input_dict['GPUID']
os.environ["CUDA_VISIBLE_DEVICES"] = gup_id
window_size = input_dict['windsize']
im_bands = input_dict['im_bands']
im_type = input_dict['dtype']
FLAG_APPROACH_PREDICT = input_dict['strategy']
input_path = input_dict['image_dir']
model_file = input_dict['model_file']
output_path = input_dict['mask_dir']
out_bands = input_dict['target_num']
all_files, num = get_file(input_path)
if num == 0:
print("There is no file in path:{}".format(input_path))
sys.exit(-1)
for img_file in all_files:
print("[INFO] opening image...".format(img_file))
input_img = load_img_by_gdal(img_file)
if im_type == UINT8:
input_img = input_img / 255.0
elif im_type == UINT10:
input_img = input_img / 1024.0
elif im_type == UINT16:
input_img = input_img / 65535.0
input_img = np.clip(input_img, 0.0, 1.0)
input_img = input_img.astype(np.float16)
model = load_model(model_file)
abs_filename = os.path.split(img_file)[1]
abs_filename = abs_filename.split(".")[0]
if FLAG_APPROACH_PREDICT == 0:
print("[INFO] predict image by orignal approach\n")
result = orignal_predict_onehot(input_img, im_bands, model, window_size)
output_file = ''.join([output_path, '/', abs_filename, '.png'])
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result * 128)
elif FLAG_APPROACH_PREDICT == 1:
print("[INFO] predict image by smooth approach\n")
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=out_bands, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_multiclass,
PLOT_PROGRESS=False
)
H, W, C = np.array(input_img).shape
output_file = ''.join([output_path, '/', abs_filename, '_smooth_pred.png'])
output_mask = np.zeros((H, W), np.uint8)
for i in range(out_bands):
indx = np.where(result[:, :, i] >= 127)
output_mask[indx] = i + 1
print(np.unique(result))
cv2.imwrite(output_file, output_mask)
print("Saved to:{}".format(output_file))
# for b in range(out_bands):
# output_file = ''.join([output_path, '/', abs_filename, '_', dict_target[b],'smooth.png'])
# cv2.imwrite(output_file, result[:,:,b])
# print("Saved to: {}".format(output_file))
gc.collect()
return 0
def predict_binary_for_batch_image(input_dict={}):
gup_id = input_dict['GPUID']
os.environ["CUDA_VISIBLE_DEVICES"] = gup_id
window_size = input_dict['windsize']
im_bands = input_dict['im_bands']
im_type = input_dict['dtype']
FLAG_APPROACH_PREDICT = input_dict['strategy']
input_path = input_dict['image_dir']
model_file = input_dict['model_file']
output_path = input_dict['mask_dir']
out_bands = 1
FLAG_ONEHOT = 0
if input_dict['onehot']:
FLAG_ONEHOT = 1
all_files, num = get_file(input_path)
if num == 0:
print("There is no file in path:{}".format(input_path))
sys.exit(-1)
for img_file in all_files:
print("[INFO] opening image...")
print("FileName:{}".format(img_file))
input_img = load_img_by_gdal(img_file)
if im_type == UINT8:
input_img = input_img / 255.0
elif im_type == UINT10:
input_img = input_img / 1024.0
elif im_type == UINT16:
input_img = input_img / 65535.0
input_img = np.clip(input_img, 0.0, 1.0)
input_img = input_img.astype(np.float16)
model = load_model(model_file)
abs_filename = os.path.split(img_file)[1]
abs_filename = abs_filename.split(".")[0]
if FLAG_APPROACH_PREDICT == 0:
print("[INFO] predict image by orignal approach\n")
if FLAG_ONEHOT:
result = orignal_predict_onehot(input_img, im_bands, model, window_size)
else:
result = orignal_predict_notonehot(input_img, im_bands, model, window_size)
output_file = ''.join([output_path, '/', abs_filename, '.png'])
print("result save as to: {}".format(output_file))
cv2.imwrite(output_file, result * 128)
elif FLAG_APPROACH_PREDICT == 1:
print("[INFO] predict image by smooth approach\n")
if FLAG_ONEHOT:
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=out_bands, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_binary_onehot,
PLOT_PROGRESS=False
)
else:
result = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=out_bands, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_binary_notonehot,
PLOT_PROGRESS=False
)
output_file = ''.join([output_path, '/', abs_filename, 'smooth.png'])
cv2.imwrite(output_file, result)
print("Saved to: {}".format(output_file))
gc.collect()
return 0
result_channels)
for key, val in multi_dict.items():
output_file = output_mask + key + '.png'
cv2.imwrite(
output_file,
result_test[:, :,
val - 1]) # achieve the integer automatically
else:
print("[INFO]sooth predict")
if FLAG_USING_MODEL == 0:
print("unet or fcnnet binary")
predictions_smooth = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=result_channels, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_unet_binary
# labelencoder=labelencoder
)
cv2.imwrite(output_mask, predictions_smooth)
elif FLAG_USING_MODEL == 1:
print("unet or fcn multiclass")
predictions_smooth = predict_img_with_smooth_windowing_multiclassbands(
input_img,
model,
window_size=window_size,
subdivisions=2,
real_classes=result_channels, # output channels = 是真的类别，总类别-背景
pred_func=smooth_predict_for_unet_multiclass