Hi , I am making a classifier which basically scans the historical places
and detects them .I am using SIFT for taking features of images and then
passing them to the neural network . Features are 128 dimensional and model
is trained with 97% accuracy but when i am using Single image prediction it
is giving error :
* ValueError: Input 0 of layer sequential_9 is incompatible with the
layer: expected axis -1 of input shape to have value 128 but received input
with shape [32, 1]*
It's shape is already 128 , but I don't know why it is giving an error .
Kindly help me i got stucked in it for several days
import cv2
import numpy as np
import tensorflow as tf
# import keras
# from keras.models import load_model
# from keras.utils import CustomObjectScope
# print(tf.__version__)
# print(keras.__version__)
Categories = ["Deewane aam", "Lahore Fort Museum", "Moti masjid", "Sheesh
Mahal"]
sift = cv2.xfeatures2d.SIFT_create()
def prepare(filepath):
IMG_SIZE = (124, 124)
img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, IMG_SIZE)
keyImage, desImage = sift.detectAndCompute(new_array, None)
feat = np.sum(desImage, axis=0)
return feat
# from keras.initializers import glorot_uniform
# with CustomObjectScope({'GlorotUniform': glorot_uniform()}):
model = tf.keras.models.load_model("SubClassPredictions.h5")
print(model.summary())
prediction = model.predict([prepare('E:\Python Telusko\OpenCv\motimasjid.jpg')])
# print(prediction)
# print(Categories[int(prediction[0][0])])
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