When I am uploading a picture to check a picture according to tensorflow h5
model, I am loading image using load_model
of tensorflow.keras.models
but it is not accepting. For JPG, it is showing TypeError: expected str, bytes or os.PathLike object, not JpegImageFile
and for PNG, it is showing as TypeError: expected str, bytes or os.PathLike object, not PngImageFile
. What to do now?
I tried the code with raw python but it worked nicely.
Code:
#views.py
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
pic = request.FILES['image']
img = Image.open(pic)
detection=load_model(os.path.join(settings.BASE_DIR,'static/auto_chloro_model.h5'))
test_img=image.load_img(img,target_size=(48,48))
test_img=image.img_to_array(test_img)
test_img=np.expand_dims(test_img,axis=0)
result=detection.predict(test_img)
a=result.argmax()
print(a)
#models.py
class images(models.Model):
img_main = models.ImageField(upload_to="images_api", default="")
def __str__(self):
return self.product_name
#forms.py
class imageForm(forms.ModelForm):
image = forms.ImageField()
class Meta:
model = images
fields = ['image']
Traceback:
Internal Server Error: /upload Traceback (most recent call last): File "C:\Users\joyan\.conda\envs\tensorflow-django\lib\site-packages\django\core\handlers\exception.py", line 47, in inner response = get_response(request) File "C:\Users\joyan\.conda\envs\tensorflow-django\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response response = wrapped_callback(request, *callback_args, **callback_kwargs) File "H:\Projects Programming Projects\Auto Chloro\plant\detection\views.py", line 50, in uploadImage test_img=image.load_img(img,target_size=(48,48)) File "C:\Users\joyan\.conda\envs\tensorflow-django\lib\site-packages\keras_preprocessing\image\utils.py", line 113, in load_img with open(path, 'rb') as f: TypeError: expected str, bytes or os.PathLike object, not JpegImageFile
CodePudding user response:
The load_img
function expects path to the image, not PIL image format.
So instead of
img = Image.open(pic)
test_img=image.load_img(img,target_size=(48,48))
You can save the image, and then get url/path to image and then give path to image.load_img
using :
from tensorflow.keras.preprocessing import image
from django.core.files.storage import default_storage
# test_img = image.load_img('/path/to/image.jpg', target_size = (48, 48))
file_name = "pic.jpg" # The file name with which it saves
file = default_storage.save(file_name, f)
file_url = default_storage.url(file)
test_img = image.load_img(file_url, target_size=(48, 48))
CodePudding user response:
This solved my problem.
pic = request.FILES['image']
img_new = images(img_main= pic)
img_new.save()
detection=load_model(os.path.join(settings.BASE_DIR,'staticfiles/auto_chloro_model.h5'))
test_img=image.load_img(img_new.img_main.path,target_size=(48,48))
test_img=image.img_to_array(test_img)
test_img=np.expand_dims(test_img,axis=0)
result=detection.predict(test_img)
a=result.argmax()