bedapudi6788
commented
4 years ago I would suggest using python 3.6.x and keras==2.2.4, tensorflow==1.14
These are the versions i tested the model with.
Closed matthewgdv closed 3 years ago
bedapudi6788
commented
4 years ago I would suggest using python 3.6.x and keras==2.2.4, tensorflow==1.14
These are the versions i tested the model with.
matthewgdv
commented
4 years ago Heya @bedapudi6788, thanks for your help!
I've just tried downgrading my version of tensorflow from 2.3.0 to 1.14 as you've suggested, and I'm getting this error:
C:\Users\matthewgdv>pip install tensorflow==1.14 --upgrade
ERROR: Could not find a version that satisfies the requirement tensorflow==1.14 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0)
ERROR: No matching distribution found for tensorflow==1.14Is there any chance the tensorflow team has removed that version? If so, how would you recommend setting up a working environment for NudeNet?
I'm sorry. I realize environment issues are literally the worst and just the most boring and obnoxious thing ever. :(
bedapudi6788
commented
4 years ago I think that's because of the python version being higher.
https://github.com/notAI-tech/LogoDet/blob/master/requirements.txt
These versions might work for your python version.
wget https://raw.githubusercontent.com/notAI-tech/LogoDet/master/requirements.txt
pip install -r requirements.txtWith those versions the NudeClassifier is now working, but the NudeDetector is now erroring like so:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-13-c0ac40df705b> in <module>
----> 1 detector = NudeDetector()
~\Python\portable\python\Lib\site-packages\nudenet\detector.py in __init__(self, model_name)
76 pydload.dload(classes_url, save_to_path=classes_path, max_time=None)
77
---> 78 self.detection_model = models.load_model(
79 checkpoint_path, backbone_name="resnet50"
80 )
~\Python\portable\python\Lib\site-packages\keras_retinanet\models\__init__.py in load_model(filepath, backbone_name)
81 """
82 import keras.models
---> 83 return keras.models.load_model(filepath, custom_objects=backbone(backbone_name).custom_objects)
84
85
~\Python\portable\python\Lib\site-packages\keras\engine\saving.py in load_wrapper(*args, **kwargs)
490 os.remove(tmp_filepath)
491 return res
--> 492 return load_function(*args, **kwargs)
493
494 return load_wrapper
~\Python\portable\python\Lib\site-packages\keras\engine\saving.py in load_model(filepath, custom_objects, compile)
582 if H5Dict.is_supported_type(filepath):
583 with H5Dict(filepath, mode='r') as h5dict:
--> 584 model = _deserialize_model(h5dict, custom_objects, compile)
585 elif hasattr(filepath, 'write') and callable(filepath.write):
586 def load_function(h5file):
~\Python\portable\python\Lib\site-packages\keras\engine\saving.py in _deserialize_model(h5dict, custom_objects, compile)
272 raise ValueError('No model found in config.')
273 model_config = json.loads(model_config.decode('utf-8'))
--> 274 model = model_from_config(model_config, custom_objects=custom_objects)
275 model_weights_group = h5dict['model_weights']
276
~\Python\portable\python\Lib\site-packages\keras\engine\saving.py in model_from_config(config, custom_objects)
625 '`Sequential.from_config(config)`?')
626 from ..layers import deserialize
--> 627 return deserialize(config, custom_objects=custom_objects)
628
629
~\Python\portable\python\Lib\site-packages\keras\layers\__init__.py in deserialize(config, custom_objects)
163 globs['Model'] = models.Model
164 globs['Sequential'] = models.Sequential
--> 165 return deserialize_keras_object(config,
166 module_objects=globs,
167 custom_objects=custom_objects,
~\Python\portable\python\Lib\site-packages\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
142 custom_objects = custom_objects or {}
143 if has_arg(cls.from_config, 'custom_objects'):
--> 144 return cls.from_config(
145 config['config'],
146 custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
~\Python\portable\python\Lib\site-packages\keras\engine\network.py in from_config(cls, config, custom_objects)
1073 node_data = node_data_list[node_index]
1074 try:
-> 1075 process_node(layer, node_data)
1076
1077 # If the node does not have all inbound layers
~\Python\portable\python\Lib\site-packages\keras\engine\network.py in process_node(layer, node_data)
1023 # and building the layer if needed.
1024 if input_tensors:
-> 1025 layer(unpack_singleton(input_tensors), **kwargs)
1026
1027 def process_layer(layer_data):
~\Python\portable\python\Lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)
~\Python\portable\python\Lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
487 # Actually call the layer,
488 # collecting output(s), mask(s), and shape(s).
--> 489 output = self.call(inputs, **kwargs)
490 output_mask = self.compute_mask(inputs, previous_mask)
491
~\Python\portable\python\Lib\site-packages\keras_retinanet\layers\_misc.py in call(self, inputs, **kwargs)
107 return output
108 else:
--> 109 return backend.resize_images(source, (target_shape[1], target_shape[2]), method='nearest')
110
111 def compute_output_shape(self, input_shape):
~\Python\portable\python\Lib\site-packages\keras_retinanet\backend\tensorflow_backend.py in resize_images(images, size, method, align_corners)
66 'area' : tensorflow.image.ResizeMethod.AREA,
67 }
---> 68 return tensorflow.image.resize_images(images, size, methods[method], align_corners)
69
70
AttributeError: module 'tensorflow._api.v2.image' has no attribute 'resize_images'I'll try and set up a Python 3.6 environment tomorrow to test it out. That said, I do suspect that it will work and that it's down to spooky interactions between the Keras/Tensorflow dependencies on newer versions of Python.
It's your call of course whether you want to support newer versions of the language or not, but I'd be really grateful if you did. I pretty much religiously upgrade my environments to the newest interpreter version usually within a month of release (unless some core dependencies I use break and need more time to catch up), and I dislike spinning up virtualenvs on older versions, since I hate having to consciously censor my code and avoid using awesome new language features (the most recent awesome one was assignment expressions <3 ) just to accomodate a dependancy.
Granted, not everyone is like me, and you for sure don't have to cater to us, but it would be awesome if you did streamline the process of using NudeNet with an up-to-date interpreter. I think over time it might also give the project more longevity.
Anyways, thanks again!
bedapudi6788
commented
4 years ago with https://github.com/notAI-tech/LogoDet/blob/master/requirements.txt these requirements, detector should have worked.
if you don't mind, can you do the following and check once
pip uninstall keras-retinanet
wget https://raw.githubusercontent.com/notAI-tech/LogoDet/master/requirements.txt
pip install -r requirements.txt
Adjenz
commented
4 years ago with https://github.com/notAI-tech/LogoDet/blob/master/requirements.txt these requirements, detector should have worked.
if you don't mind, can you do the following and check once
pip uninstall keras-retinanet wget https://raw.githubusercontent.com/notAI-tech/LogoDet/master/requirements.txt pip install -r requirements.txt
Hi mate, I have that issue too.
I installed all the requirements with the good versions. This is what it shows :
>>> from nudenet import NudeClassifier Using TensorFlow backend. c>>> classifier = NudeClassifier() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/adrien/.local/lib/python3.8/site-packages/nudenet/classifier.py", line 139, in __init__ self.nsfw_model = keras.models.load_model(model_path) File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/saving.py", line 419, in load_model model = _deserialize_model(f, custom_objects, compile) File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/saving.py", line 225, in _deserialize_model model = model_from_config(model_config, custom_objects=custom_objects) File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/saving.py", line 458, in model_from_config return deserialize(config, custom_objects=custom_objects) File "/home/adrien/.local/lib/python3.8/site-packages/keras/layers/__init__.py", line 52, in deserialize return deserialize_keras_object(config, File "/home/adrien/.local/lib/python3.8/site-packages/keras/utils/generic_utils.py", line 142, in deserialize_keras_object return cls.from_config( File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/network.py", line 1022, in from_config process_layer(layer_data) File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/network.py", line 1007, in process_layer layer = deserialize_layer(layer_data, File "/home/adrien/.local/lib/python3.8/site-packages/keras/layers/__init__.py", line 52, in deserialize return deserialize_keras_object(config, File "/home/adrien/.local/lib/python3.8/site-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object return cls.from_config(config['config']) File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1109, in from_config return cls(**config) File "/home/adrien/.local/lib/python3.8/site-packages/keras/legacy/interfaces.py", line 91, in wrapper return func(*args, **kwargs) File "/home/adrien/.local/lib/python3.8/site-packages/keras/engine/input_layer.py", line 84, in __init__ input_tensor = K.placeholder(shape=batch_input_shape, File "/home/adrien/.local/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py", line 517, in placeholder x = tf.placeholder(dtype, shape=shape, name=name) AttributeError: module 'tensorflow' has no attribute 'placeholder'
Adjenz
commented
4 years ago Same thing on Google Colab :
`AttributeError Traceback (most recent call last)
bedapudi6788
commented
4 years ago Sorry, at the moment you will have to use it in a venv with tf 1.14 and keras 2.24. Or, using it as rest API via docker is a good idea if you don't want to create new venv.
I will try to support newer versions in future.
Adjenz
commented
4 years ago Sorry, at the moment you will have to use it in a venv with tf 1.14 and keras 2.24. Or, using it as rest API via docker is a good idea if you don't want to create new venv.
I will try to support newer versions in future.
Ok then I'll just learn how to use Docker. Thanks mate.
entrpn
commented
4 years ago You can use this with tf2 but you need to clone the project and in classifier.py you need to add to the top of the file
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()And then instead of using keras, you import tf.keras.
Full file change:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import os
import cv2
#import keras
import pydload
import logging
import numpy as np
from .video_utils import get_interest_frames_from_video
from PIL import Image as pil_image
if pil_image is not None:
_PIL_INTERPOLATION_METHODS = {
"nearest": pil_image.NEAREST,
"bilinear": pil_image.BILINEAR,
"bicubic": pil_image.BICUBIC,
}
# These methods were only introduced in version 3.4.0 (2016).
if hasattr(pil_image, "HAMMING"):
_PIL_INTERPOLATION_METHODS["hamming"] = pil_image.HAMMING
if hasattr(pil_image, "BOX"):
_PIL_INTERPOLATION_METHODS["box"] = pil_image.BOX
# This method is new in version 1.1.3 (2013).
if hasattr(pil_image, "LANCZOS"):
_PIL_INTERPOLATION_METHODS["lanczos"] = pil_image.LANCZOS
def load_img(
path, grayscale=False, color_mode="rgb", target_size=None, interpolation="nearest"
):
"""Loads an image into PIL format.
:param path: Path to image file.
:param grayscale: DEPRECATED use `color_mode="grayscale"`.
:param color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
The desired image format.
:param target_size: Either `None` (default to original size)
or tuple of ints `(img_height, img_width)`.
:param interpolation: Interpolation method used to resample the image if the
target size is different from that of the loaded image.
Supported methods are "nearest", "bilinear", and "bicubic".
If PIL version 1.1.3 or newer is installed, "lanczos" is also
supported. If PIL version 3.4.0 or newer is installed, "box" and
"hamming" are also supported. By default, "nearest" is used.
:return: A PIL Image instance.
"""
if grayscale is True:
logging.warn("grayscale is deprecated. Please use " 'color_mode = "grayscale"')
color_mode = "grayscale"
if pil_image is None:
raise ImportError(
"Could not import PIL.Image. " "The use of `load_img` requires PIL."
)
if isinstance(path, type("")):
img = pil_image.open(path)
else:
path = cv2.cvtColor(path, cv2.COLOR_BGR2RGB)
img = pil_image.fromarray(path)
if color_mode == "grayscale":
if img.mode != "L":
img = img.convert("L")
elif color_mode == "rgba":
if img.mode != "RGBA":
img = img.convert("RGBA")
elif color_mode == "rgb":
if img.mode != "RGB":
img = img.convert("RGB")
else:
raise ValueError('color_mode must be "grayscale", "rgb", or "rgba"')
if target_size is not None:
width_height_tuple = (target_size[1], target_size[0])
if img.size != width_height_tuple:
if interpolation not in _PIL_INTERPOLATION_METHODS:
raise ValueError(
"Invalid interpolation method {} specified. Supported "
"methods are {}".format(
interpolation, ", ".join(_PIL_INTERPOLATION_METHODS.keys())
)
)
resample = _PIL_INTERPOLATION_METHODS[interpolation]
img = img.resize(width_height_tuple, resample)
return img
def load_images(image_paths, image_size, image_names):
"""
Function for loading images into numpy arrays for passing to model.predict
inputs:
image_paths: list of image paths to load
image_size: size into which images should be resized
outputs:
loaded_images: loaded images on which keras model can run predictions
loaded_image_indexes: paths of images which the function is able to process
"""
loaded_images = []
loaded_image_paths = []
for i, img_path in enumerate(image_paths):
try:
image = load_img(img_path, target_size=image_size)
image = tf.keras.preprocessing.image.img_to_array(image)
image /= 255
loaded_images.append(image)
loaded_image_paths.append(image_names[i])
except Exception as ex:
logging.exception(f"Error reading {img_path} {ex}", exc_info=True)
return np.asarray(loaded_images), loaded_image_paths
class Classifier:
"""
Class for loading model and running predictions.
For example on how to use take a look the if __name__ == '__main__' part.
"""
nsfw_model = None
def __init__(self):
"""
model = Classifier()
"""
url = "https://github.com/bedapudi6788/NudeNet/releases/download/v0/classifier_model"
home = os.path.expanduser("~")
model_folder = os.path.join(home, ".NudeNet/")
if not os.path.exists(model_folder):
os.mkdir(model_folder)
model_path = os.path.join(model_folder, "classifier")
if not os.path.exists(model_path):
print("Downloading the checkpoint to", model_path)
pydload.dload(url, save_to_path=model_path, max_time=None)
self.nsfw_model = tf.keras.models.load_model(model_path)
def classify_video(
self,
video_path,
batch_size=4,
image_size=(256, 256),
categories=["unsafe", "safe"],
):
frame_indices = None
frame_indices, frames, fps, video_length = get_interest_frames_from_video(
video_path
)
logging.debug(
f"VIDEO_PATH: {video_path}, FPS: {fps}, Important frame indices: {frame_indices}, Video length: {video_length}"
)
frames, frame_names = load_images(frames, image_size, image_names=frame_indices)
if not frame_names:
return {}
model_preds = self.nsfw_model.predict(frames, batch_size=batch_size)
preds = np.argsort(model_preds, axis=1).tolist()
probs = []
for i, single_preds in enumerate(preds):
single_probs = []
for j, pred in enumerate(single_preds):
single_probs.append(model_preds[i][pred])
preds[i][j] = categories[pred]
probs.append(single_probs)
return_preds = {
"metadata": {
"fps": fps,
"video_length": video_length,
"video_path": video_path,
},
"preds": {},
}
for i, frame_name in enumerate(frame_names):
return_preds["preds"][frame_name] = {}
for _ in range(len(preds[i])):
return_preds["preds"][frame_name][preds[i][_]] = probs[i][_]
return return_preds
def classify(
self,
image_paths=[],
batch_size=4,
image_size=(256, 256),
categories=["unsafe", "safe"],
):
"""
inputs:
image_paths: list of image paths or can be a string too (for single image)
batch_size: batch_size for running predictions
image_size: size to which the image needs to be resized
categories: since the model predicts numbers, categories is the list of actual names of categories
"""
if isinstance(image_paths, str):
image_paths = [image_paths]
loaded_images, loaded_image_paths = load_images(
image_paths, image_size, image_names=image_paths
)
if not loaded_image_paths:
return {}
model_preds = self.nsfw_model.predict(
loaded_images, batch_size=batch_size
)
preds = np.argsort(model_preds, axis=1).tolist()
probs = []
for i, single_preds in enumerate(preds):
single_probs = []
for j, pred in enumerate(single_preds):
single_probs.append(model_preds[i][pred])
preds[i][j] = categories[pred]
probs.append(single_probs)
images_preds = {}
for i, loaded_image_path in enumerate(loaded_image_paths):
if not isinstance(loaded_image_path, str):
loaded_image_path = i
images_preds[loaded_image_path] = {}
for _ in range(len(preds[i])):
images_preds[loaded_image_path][preds[i][_]] = probs[i][_]
return images_preds
if __name__ == "__main__":
print(
'\n Enter path for the keras weights, leave empty to use "./nsfw.299x299.h5" \n'
)
weights_path = input().strip()
if not weights_path:
weights_path = "../nsfw.299x299.h5"
m = Classifier()
while 1:
print(
"\n Enter single image path or multiple images seperated by || (2 pipes) \n"
)
images = input().split("||")
images = [image.strip() for image in images]
print(m.predict(images), "\n")
Btw, thank you for this repo. Saved me so much time.
bedapudi6788
commented
3 years ago @matthewgdv @Screamus57 @entrpn from the current release, nudenet doesn't require any version of tensorflow and will work as expected on all newer python versions.
This is the stack trace I'm getting when trying to instanciate a
NudeClassifier. A pretty much identical error occurs when trying to instanciate aNudeDetector.I've googled around and these seem to be issues with Keras that have since been fixed? Basically, when I use keras==2.2.4 (which is listed in this library's setup.py) I can't get past this stage.
I've tried upgrading to the latest version of keras for my own curiosity (keras==2.4.3) and the
NudeClassifierthen works perfectly! However, theNudeDetectornow errors like this when I try to instanciate it:Do you know why this is happening? I'd love to play around with this library but I can't figure out how to get past these errors.
If it helps, I'm running Python 3.8.2
Thanks in advance!