def process(input_path, output_path, model_name="u2net",
preprocessing_method_name="bbd-fastrcnn", postprocessing_method_name="rtb-bnb"):
"""
Processes the file.
:param input_path: The path to the image / folder with the images to be processed.
:param output_path: The path to the save location.
:param model_name: Model to use.
:param postprocessing_method_name: Method for image preprocessing
:param preprocessing_method_name: Method for image post-processing
"""
if input_path is None or output_path is None:
raise Exception("Bad parameters! Please specify input path and output path.")
model = model_detect(model_name) # Load model
if not model:
logger.warning("Warning! You specified an invalid model type. "
"For image processing, the model with the best processing quality will be used. "
"(u2net)")
model_name = "u2net" # If the model line is wrong, select the model with better quality.
model = model_detect(model_name) # Load model
preprocessing_method = preprocessing.method_detect(preprocessing_method_name)
postprocessing_method = postprocessing.method_detect(postprocessing_method_name)
wmode = __work_mode__(input_path) # Get work mode
if wmode == "file": # File work mode
image = model.process_image(input_path, preprocessing_method, postprocessing_method)
__save_image_file__(image, os.path.basename(input_path), output_path, wmode)
elif wmode == "dir": # Dir work mode
# Start process
files = os.listdir(input_path)
for file in tqdm.tqdm(files, ascii=True, desc='Remove Background', unit='image'):
file_path = os.path.join(input_path, file)
image = model.process_image(file_path, preprocessing_method, postprocessing_method)
__save_image_file__(image, file, output_path, wmode)
else:
raise Exception("Bad input parameter! Please indicate the correct path to the file or folder.")
def process(input_path,
output_path,
model_name="u2net",
preprocessing_method_name="bbd-fastrcnn",
postprocessing_method_name="rtb-bnb"):
"""
Processes the file.
:param input_path: The path to the image / folder with the images to be processed.
:param output_path: The path to the save location.
:param model_name: Model to use.
:param postprocessing_method_name: Method for image preprocessing
:param preprocessing_method_name: Method for image post-processing
"""
# if input_path is None or output_path is None:
# raise Exception("Bad parameters! Please specify input path and output path.")
model = model_detect(model_name) # Load model
if not model:
logger.warning(
"Warning! You specified an invalid model type. "
"For image processing, the model with the best processing quality will be used. "
"(u2net)")
model_name = "u2net" # If the model line is wrong, select the model with better quality.
model = model_detect(model_name) # Load model
preprocessing_method = preprocessing.method_detect(
preprocessing_method_name)
postprocessing_method = postprocessing.method_detect(
postprocessing_method_name)
folder = "images"
if not os.path.exists(folder):
os.mkdir(folder)
print("img bg rm service is running!")
while True:
time.sleep(0.5)
img_files = [
f for f in listdir(folder)
if isfile(join(folder, f)) and f.endswith("jpg")
]
if len(img_files) == 0:
continue
img_files.sort()
# has images to rm bg
for img in img_files:
input_path = join(folder, img)
output_path = join(folder, img.replace("jpg", "png"))
img = cv2.imread(input_path)
if img is None:
# check valid img file
continue
print(f"processing ", input_path)
image = model.process_image(input_path, preprocessing_method,
postprocessing_method)
__save_image_file__(image, os.path.basename(input_path),
output_path, "file")
os.remove(input_path)
def save():
start = time.time()
logging.info(' CUT')
print("here")
# Convert string of image data to uint8.
print(request.files)
if 'data' not in request.files:
return jsonify({
'status': 'error',
'error': 'missing file param `data`'
}), 400
data = request.files['data'].read()
if len(data) == 0:
return jsonify({'status:': 'error', 'error': 'empty image'}), 400
# Save debug locally.
with open('imgtmp.jpg', 'wb') as f:
f.write(data)
image = Image.open("imgtmp.jpg")
print(image.size)
input_path = 'imgtmp.jpg'
output_path = 'imgtmp.png'
model_name = 'u2net'
preprocessing_method_name = 'none'
postprocessing_method_name = 'fba'
model = model_detect(model_name) # Load model
print("--model start--")
preprocessing_method = preprocessing.method_detect(
preprocessing_method_name)
postprocessing_method = postprocessing.method_detect(
postprocessing_method_name)
wmode = "file" # Get work mode
image = model.process_image(input_path, preprocessing_method,
postprocessing_method)
image.save('imgtmp.png')
with open("imgtmp.png", "rb") as img_file:
my_string = base64.b64encode(img_file.read())
socketio.emit('req', {'base64': my_string})
# Save to buffer
buff = io.BytesIO()
image.save(buff, 'PNG')
buff.seek(0)
print("--model end--")
# Print stats
logging.info(f'Completed in {time.time() - start:.2f}s')
print(f'Completed in {time.time() - start:.2f}s')
return send_file(buff, mimetype='image/png')
def process(input_path, output_path, model_name="u2netp",model_dir='models',
preprocessing_method_name=None, postprocessing_method_name="rtb-bnb"):
"""
Processes the file.
:param input_path: The path to the image / folder with the images to be processed.
:param output_path: The path to the save location.
:param model_name: Model to use.
:param postprocessing_method_name: Method for image preprocessing
:param preprocessing_method_name: Method for image post-processing
"""
if input_path is None or output_path is None:
raise Exception("Bad parameters! Please specify input path and output path.")
model = model_detect(model_name,model_dir) # Load model
if not model:
logger.warning("Warning! You specified an invalid model type. "
"For image processing, the model with the best processing quality will be used. "
"(u2net)")
model_name = "u2net" # If the model line is wrong, select the model with better quality.
model = model_detect(model_name) # Load model
preprocessing_method = preprocessing.method_detect(preprocessing_method_name)
postprocessing_method = postprocessing.method_detect(postprocessing_method_name)
image = model.process_image(input_path, preprocessing_method, postprocessing_method)
return image
def test_postprocess_method(self):
for method in strings.POSTPROCESS_METHODS:
method = postprocessing.method_detect(method)
if method is False:
self.fail("Unknown model in POSTPROCESS_METHODS!!!")
def process(input_path, output_path, model_name="u2net",\
preprocessing_method_name="bbd-fastrcnn", postprocessing_method_name="rtb-bnb"):
"""
Processes the file.
:param input_path: The path to the image / folder with the images to be processed.
:param output_path: The path to the save location.
:param model_name: Model to use.
:param postprocessing_method_name: Method for image preprocessing
:param preprocessing_method_name: Method for image post-processing
"""
logging.info("process.task >>> started")
returnData = False
if input_path is None or output_path is None:
raise Exception(
"Bad parameters! Please specify input path and output path.")
model = model_detect(model_name) # Load model
logging.info("going to run process by model %s" % model)
if not model:
logging.info(
"Warning! You specified an invalid model type. "
"For image processing, the model with the best processing quality will be used. "
"(u2net)")
model_name = "u2net" # If the model line is wrong, select the model with better quality.
logging.info("model name is {}".format(model_name))
model = model_detect(model_name) # Load model
logging.info("preprocessing_method background removal process started")
preprocessing_method = preprocessing.method_detect(
preprocessing_method_name)
logging.info("postprocessing_method background process started")
postprocessing_method = postprocessing.method_detect(
postprocessing_method_name)
wmode = __work_mode__(input_path) # Get work mode
logging.info(">>>task.process processing completed. task.status success")
returnData = False
if wmode == "file": # File work mode
image = model.process_image(input_path, preprocessing_method,
postprocessing_method)
__save_image_file__(image, os.path.basename(input_path), output_path,
wmode)
returnData = True
logging.info("sucess.task >>>> True ")
elif wmode == "dir": # Dir work mode
# Start process
files = os.listdir(input_path)
for file in tqdm.tqdm(files,
ascii=True,
desc='Remove Background',
unit='image'):
file_path = os.path.join(input_path, file)
image = model.process_image(file_path, preprocessing_method,
postprocessing_method)
__save_image_file__(image, file, output_path, wmode)
logging.info("sucess.task >>>> True ")
returnData = True
else:
logging.info(
"Bad input parameter! Please indicate the correct path to the file or folder."
)
returnData = False
logging.info("task.status >>>> failed")
print("Image Editor task.status True")
print(returnData)
if returnData is True:
url = os.getenv("APIURL", "Error Found in url")
logging.info("API url >> {}".format(url))
image = finalresponse[0]
logging.info("o/p image and file id is {} ".format(image, fileId))
# now = datetime.now()
# createdBy = now.strftime("%d/%m/%Y %H:%M:%S")
data = MultipartEncoder(
fields={
'id': fileId,
'file': (os.path.basename(image), open(image, 'rb'),
'text/plain')
})
logging.info("content type is {}".format(data.content_type))
response = requests.post(url,
data=data,
headers={'Content-Type': data.content_type})
logging.info(response.status_code)
json_res = json.loads(response.text)
print(json_res)
logging.info("API response {}".format(json_res))
if response.status_code != 200:
#payload["error"] = logs
logging.error("error found %s" % str(logs), 100, ex=True)
else:
logging.info("total operation performed {}".format(logs))
logging.info("API response.status success")
#responseApi = requests.post(url=url, data=payload, headers=headers)
#logging.info(" {}".format(json.dumps(responseApi.text)))
#}
else:
logging.info("script task.status >> failed")