This is sentiment analysis project and i am getting this error
Nudity-Detection-Model.h5
Traceback (most recent call last):
File "c:\Users\kvidushi\Desktop\Mini_project\script\vapp.py", line 214, in <module>
main()
File "c:\Users\kvidushi\Desktop\Mini_project\script\vapp.py", line 208, in main
model = load_model('Nudity-Detection-Model.h5')
File "c:\Users\kvidushi\Desktop\Mini_project\script\vapp.py", line 59, in load_model
raise ValueError("saved_model_path must be the valid directory of a saved model to load.")
ValueError: saved_model_path must be the valid directory of a saved model to load.
My script file is:
import json
import cv2
import os
import time
from os import listdir
from os.path import isfile, join, exists, isdir, abspath
from keras.models import load_model
import numpy as np
import tensorflow as tf
from tensorflow import keras
import tensorflow_hub as hub
import matplotlib.pyplot as plt
IMAGE_DIM = 224 # required/default image dimensionality
def load_images(image_paths, image_size, verbose=True):
# 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
# verbose: show all of the image path and sizes loaded
# 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 = []
if isdir(image_paths):
parent = abspath(image_paths)
image_paths = [join(parent, f) for f in listdir(image_paths) if isfile(join(parent, f))]
elif isfile(image_paths):
image_paths = [image_paths]
for img_path in image_paths:
try:
if verbose:
print(img_path, "size:", image_size)
image = keras.preprocessing.image.load_img(img_path, target_size=image_size)
image = keras.preprocessing.image.img_to_array(image)
# print(image.dtype)
# print(image.shape)
# print(image)
image /= 255
loaded_images.append(image)
loaded_image_paths.append(img_path)
except Exception as ex:
print("Image Load Failure: ", img_path, ex)
return np.asarray(loaded_images), loaded_image_paths
def load_model(model_path):
print(model_path)
if model_path is None or not exists(model_path):
raise ValueError("saved_model_path must be the valid directory of a saved model to load.")
model = tf.keras.models.load_model(model_path)
#model = tf.keras.models.load_model(model_path, custom_objects={'KerasLayer':hub.KerasLayer})
# model.summary()
print(model.summary())
return model
def classify(model, input_paths, image_dim=IMAGE_DIM):
""" Classify given a model, input paths (could be single string), and image dimensionality...."""
images, image_paths = load_images(input_paths, (image_dim, image_dim))
probs = classify_nd(model, images)
# print(type(probs))
return probs
def classify_nd(model, nd_images):
""" Classify given a model, image array (numpy)...."""
model_preds = model.predict(nd_images)
# preds = np.argsort(model_preds, axis = 1).tolist()
categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']
probs = []
single_probs = {}
cnt=0
for i, single_preds in enumerate(model_preds):
cnt=cnt+1
for j, pred in enumerate(single_preds):
if categories[j] in single_probs.keys():
single_probs[categories[j]] = single_probs[categories[j]] + float(pred)
else:
single_probs[categories[j]]=float(pred)
print(cnt)
for i in single_probs.keys():
# print(single_probs[i])
single_probs[i]=single_probs[i]/cnt
probs.append(single_probs)
return probs
def predict(model,img_paths):
# for img in img_paths:
image_preds = classify(model, img_paths, IMAGE_DIM)
data=image_preds[0]
category= list(data.keys())
values = list(data.values())
fig = plt.figure(figsize = (10, 5))
# creating the bar plot
plt.bar(category, values, color ='maroon',
width = 0.4)
plt.xlabel("Categories")
plt.ylabel("values")
plt.title("Nudity Detection Model")
print(json.dumps(image_preds, indent=2), '\n')
plt.show()
def get_frames(inputFile,outputFolder,step,count):
'''
Input:
inputFile - name of the input file with directoy
outputFolder - name and path of the folder to save the results
step - time lapse between each step (in seconds)
count - number of screenshots
Output:
'count' number of screenshots that are 'step' seconds apart created from video 'inputFile' and stored in folder 'outputFolder'
Function Call:
get_frames("test.mp4", 'data', 10, 10)
'''
#initializing local variables
step = step
frames_count = count
currentframe = 0
frames_captured = 0
#creating a folder
try:
# creating a folder named data
if not os.path.exists(outputFolder):
os.makedirs(outputFolder)
#if not created then raise error
except OSError:
print ('Error! Could not create a directory')
#reading the video from specified path
cam = cv2.VideoCapture(inputFile)
#reading the number of frames at that particular second
frame_per_second = cam.get(cv2.CAP_PROP_FPS)
print( frame_per_second)
while (True):
ret, frame = cam.read()
if ret:
if currentframe > (step*frame_per_second):
currentframe = 0
#saving the frames (screenshots)
name = './data/frame' + str(frames_captured) + '.jpg'
print ('Creating...' + name)
cv2.imwrite(name, frame)
frames_captured+=1
#breaking the loop when count achieved
if frames_captured > frames_count-1:
ret = False
currentframe += 1
if ret == False:
break
#Releasing all space and windows once done
cam.release()
cv2.destroyAllWindows()
def main():
# img_paths=[]
# img_paths.append("1.jpg")
# img_paths.append("2.jpg")
# img_paths.append("3.jpg")
# img_paths.append("4.jpg")
# img_paths.append("5.jpg")
# img_paths.append("6.jpg")
# img_paths.append("7.jpg")
# img_paths.append("8.jpg")
# img_paths.append("9.jpg")
# img_paths.append("10.jpg")
# img_paths.append("11.jpg")
# img_paths.append("12.jpg")
# img_paths.append("13.jpg")
# img_paths.append("14.jpg")
# img_paths.append("15.jpg")
get_frames("1.mp4","data",5,20)
model = load_model('Nudity-Detection-Model.h5')
predict(model,"data")
if __name__ == "__main__":
main()
It is asking for this file: Nudity_detection_model.h5 I have put this file in same folder.Still it is not able to recognize it. I tried adding double quotes and single quotes and import load_model but still the error is same. can anyone help me
