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ValueError: saved_model_path must be the valid directory of a saved model to load

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

cv2.error when training image (Overload Resolution Failed)

I'm learning opencv via the following link link link2. And I got error related to image training process for face recognition. Please correct me or help with the problem I'm having. thank you
System Information :
OpenCV 4.5.2.52
Python 3.9.5
Detailed Description
I need to train an images by using the code below and the results was :
- OpenCV/faces-train.py", line 50, in <module>
recognizer.train(x_train, np.array(y_labels))
cv2.error: OpenCV(4.5.2) :-1: error: (-5:Bad argument) in function 'train'
> Overload resolution failed:
> - src is not a numpy array, neither a scalar
> - Expected Ptr<cv::UMat> for argument 'src'
Code
import cv2
import os
import numpy as np
from PIL import Image
import pickle
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(BASE_DIR, "images")
face_cascade = cv2.CascadeClassifier('cascade/data/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
current_id = 0
label_ids = {}
y_labels = []
x_train = []
for root, dirs, files, in os.walk(image_dir):
for file in files:
if file.endswith("png") or file.endswith("jpg") or file.endswith("jpeg") :
path = os.path.join(root, file)
label = os.path.basename(root).replace(" ", "-").lower()
# print(label, path)
if not label in label_ids:
label_ids[label] = current_id
current_id += 1
id_ = label_ids[label]
# print(label_ids)
y_labels.append(label)
x_train.append(path)
pil_image = Image.open(path).convert("L")
image_array = np.array(pil_image, "uint8")
# print(image_array)
faces = face_cascade.detectMultiScale(image_array, scaleFactor=1.5, minNeighbors=5)
for(x,y,w,h) in faces:
roi = image_array[y:y+h, x:x+w]
x_train.append(roi)
y_labels.append(id_)
# print(y_labels)
# print(x_train)
with open("labels.pickle", 'wb') as f:
pickle.dump(label_ids, f)
recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.yml")

remove the following lines:
y_labels.append(label)
x_train.append(path)
you don't really need to append the labels or the paths since you are basically training the face recognizer to recognise/categorise faces. Likewise, you wouldnt need the path to your images as it is also not necessary. Guessing this is what caused the error!
Do let me know if the fix works! Cheers :)

How to continuously update video from images that keep coming in python?

I am working on a robotics project in which a robot creates images in .pgm format (not camera images) continuously which I'm converting to .jpeg and saving to a directory. However, this directory keeps updating as more images keep getting added while I want to convert these images continuously in a video. I can already create a video from the images saved in one instance but what I want is to update the video continuously as a single file (streaming onto a URL) while images keep getting saved. Here is my code but it doesn't work in parallel for images as well as for videos. Anyone can help?
from PIL import Image
import os, glob
import cv2
import numpy as np
from os.path import isfile, join
# remove .yaml files
directory = "/home/user/catkin_ws/src/ros_map/map_pgms"
files_in_directory = os.listdir(directory)
filtered_files = [file for file in files_in_directory if file.endswith(".yaml")]
for file in filtered_files:
path_to_file = os.path.join(directory, file)
os.remove(path_to_file)
def batch_image(in_dir, out_dir):
if not os.path.exists(out_dir):
print(out_dir, 'is not existed.')
os.mkdir(out_dir)
if not os.path.exists(in_dir):
print(in_dir, 'is not existed.')
return -1
count = 0
for files in glob.glob(in_dir + '/*'):
filepath, filename = os.path.split(files)
out_file = filename[0:9] + '.jpg'
# print(filepath,',',filename, ',', out_file)
im = Image.open(files)
new_path = os.path.join(out_dir, out_file)
print(count, ',', new_path)
count = count + 1
im.save(os.path.join(out_dir, out_file))
if __name__ == '__main__':
batch_image('/home/user/catkin_ws/src/ros_map/map_pgms', './batch')
# convert the .jpgs to video
img_array = []
for filename in glob.glob('./batch/*.jpg'):
img = cv2.imread(filename)
height, width, layers = img.shape
size = (width, height)
img_array.append(img)
out = cv2.VideoWriter('project.avi', cv2.VideoWriter_fourcc(*'DIVX'), 15, size)
for i in range(len(img_array)):
out.write(img_array[i])
out.release()

OpenCV Panorama: TypeError: cannot unpack non-iterable NoneType object

I'm trying to make a panorama from a image set. I'm using Spyder, OpenCV 3.4 and Python 3.7. Here's the code:
The main:
from stitches import Stitcher
#from PIL import Image
import os
import glob
import numpy as np
import imutils
import cv2
cap = cv2.VideoCapture('C:/Users/VID_20181208_111037881.mp4')
img_dir = "C:/Users/user/images"
data_path = os.path.join(img_dir, '*g')
files = glob.glob(data_path)
args = []
for f1 in files:
img = cv2.imread(f1)
args.append(img)
def retImg(img):
return img
for i in args:
j = i+1
frame = retImg(i)
frame2 = retImg(j)
#imageA = cv2.imread(frame)
#imageB = cv2.imread(frame2)
imageA = imutils.resize(frame, width=400)
imageB = imutils.resize(frame2, width=400)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=False)
frame = result
# show the images
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
The stitch inside stitches.py:
def stitch(self, images, ratio=0.75, reprojThresh=4.0, showMatches=False):
# unpack the images, then detect keypoints and extract
# local invariant descriptors from them
(imageB, imageA) = images
(kpsA, featuresA) = self.detectAndDescribe(imageA)
(kpsB, featuresB) = self.detectAndDescribe(imageB)
# match features between the two images
M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
# if the match is None, then there aren't enough matched
# keypoints to create a panorama
if M is None:
return None
# together
(matches, H, status) = M
result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
# check to see if the keypoint matches should be visualized
if showMatches:
vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
# return a tuple of the stitched image and the
# visualization
return (result, vis)
# return the stitched image
return result
When I compile it, the following error is shown:
File "C:/Users/user/panorama.py", line 44, in <module>
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=False)
TypeError: cannot unpack non-iterable NoneType object
And I cannot understand why. I'm new to Python, so maybe this mistake is really simple, but I don't know what to do here. Thanks by advance!

resize images while loading into python

Hi: I would like load some 2400 images from a folder into python 3.6 for Neural Networks, the following code works, however, it loads images in its original size, which is (2443, 320, 400, 3), after converting into an array. How do I resize it to 64x96? so it is (2443, 64, 96, 3) and less of a load on memory. Separately, how do I do it using parallel processing, so it is efficient.
Thank you!
IMAGE_PATH = 'drive/xyz/data/'
file_paths = glob.glob(path.join(IMAGE_PATH, '*.gif'))
# Load the images
images = [misc.imread(path) for path in file_paths]
images = np.asarray(images)
Inspired by this link, I tried to do the following:
from PIL import Image
basewidth = 96
IMAGE_PATH = 'drive/xyz/data/'
file_paths = glob.glob(path.join(IMAGE_PATH, '*.gif'))
# Load the images img = [misc.imread(path) for path in file_paths]
wpercent = (basewidth/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
img = img.resize((basewidth,hsize), Image.ANTIALIAS)
images = np.asarray(img)
however, it resulted in an error, written below. Any suggestions, would be greatly appreciated. Thank you.
AttributeError Traceback (most recent call last)
<ipython-input-7-56ac1d841c56> in <module>()
9 img = [misc.imread(path) for path in file_paths]
10
---> 11 wpercent = (basewidth/float(img.size[0]))
12 hsize = int((float(img.size[1])*float(wpercent)))
13 img = img.resize((basewidth,hsize), Image.ANTIALIAS)
AttributeError: 'list' object has no attribute 'size'

First you may want to resize your images then you can use your same method to initialize the arrays given the output image folder.
Resizing the image
This uses PIL package for resizing images but any library should do the same as long as it provides a resize method.
You can read further down discussions from here How do I resize an image using PIL and maintain its aspect ratio?
import os, sys
import Image
size = 128, 128
for infile in sys.argv[1:]:
outfile = os.path.splitext(infile)[0] + ".gif"
if infile != outfile:
try:
im = Image.open(infile)
im.thumbnail(size, Image.ANTIALIAS)
im.save(outfile, "GIF")
except IOError:
print "cannot create thumbnail for '%s'" % infile
Parallelism Example
Regarding parallelism you can use this example as a base and move on from there.
This is from the python docs https://docs.python.org/3/library/multiprocessing.html
from multiprocessing import Process
def f(name):
print('hello', name)
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()