def saveParameters(fileDir):
# Model name 1 mean dataset`s folder 1.
model_name = '1'
detection_model = objectDetector.load_model(model_name)
# File is directory
files = utility.get_filenames(fileDir)
fileNames = []
domColors = []
wallColors = []
floorColors = []
for f in files:
if "." not in f:
continue
print("Now proceeding ", f, " [ ", files.index(f), " ]")
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, f)
# Save file name make.
save_file_name = utility.add_name(f, "_od", extension="bin")
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += f.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(
f, int(coord[i][0]), int(coord[i][1]), int(coord[i][2]),
int(coord[i][3]))
rect_image_name = save_image_name + f.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name, "_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
dom_color = image_processing.get_dominant_color(f)
n_color = utility.get_remarkable_color_n(dom_color, MAX_COLOR_LENGTH)
fileNames.append(os.path.basename(f))
domColors.append(n_color)
wallColors.append([])
floorColors.append([])
utility.save_result([
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
], save_file_name)
utility.save_result([files, domColors, wallColors, floorColors],
config.RESEARCH_BASE_FILE)
def select_user_roi(image_path):
'''
由于原图的分辨率较大，这里缩小后获取ROI，返回时需要重新scale对应原图
:param image_path:
:return:
'''
orig_image = image_processing.read_image(image_path)
orig_shape = np.shape(orig_image)
resize_image = image_processing.resize_image(orig_image,
resize_height=800,
resize_width=None)
re_shape = np.shape(resize_image)
g_rect = get_image_roi(resize_image)
orgi_rect = image_processing.scale_rect(g_rect, re_shape, orig_shape)
roi_image = image_processing.get_rect_image(orig_image, orgi_rect)
image_processing.cv_show_image("RECT", roi_image)
image_processing.show_image_rect("image", orig_image, orgi_rect)
return orgi_rect
def saveParameter(fileName, detection_model):
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, fileName)
# Save file name make.
save_file_name = config.RESEARCH_BASE_DIR + "/" + os.path.basename(
utility.get_od_bin(fileName))
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += fileName.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(fileName,
int(coord[i][0]),
int(coord[i][1]),
int(coord[i][2]),
int(coord[i][3]))
rect_image_name = save_image_name + fileName.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name, "_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
dom_color = image_processing.get_dominant_color(fileName)
n_color = utility.get_remarkable_color_n(dom_color, MAX_COLOR_LENGTH)
utility.save_result([
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
], save_file_name)
return [
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
]
def objectDetect(inputFile, outputFile):
'''
입력받은 inputFile의 가구를 ObjectDetection한 결과를 outputFile에 저장한다. json 형태로 저장한다.
현재는 bin file로만 입출력이 가능.
폴더를 입력하면 outputFile은 무시됨.
'''
if "." not in inputFile:
# File is directory
files = utility.get_filenames(inputFile)
for f in files:
if "." not in f:
continue
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, f)
# Save file name make.
save_file_name = utility.add_name(f, "_od", extension="bin")
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += f.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(
f, int(coord[i][0]), int(coord[i][1]), int(coord[i][2]),
int(coord[i][3]))
rect_image_name = save_image_name + f.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name,
"_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
utility.save_result([
coord, str_tag, number_tag, score, rect_files, additional_infor
], save_file_name)
else:
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, inputFile)
# Save file name make.
save_file_name = utility.add_name(inputFile, "_od", extension="bin")
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += inputFile.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(
inputFile, int(coord[i][0]), int(coord[i][1]),
int(coord[i][2]), int(coord[i][3]))
rect_image_name = save_image_name + inputFile.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name, "_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
utility.save_result(
[coord, str_tag, number_tag, score, rect_files, additional_infor],
outputFile)