Category: Misc

hey everyone so i have been experimenting with object detection using python,opencv and tensorflow but i keep getting this error P.S both the code an “myData” are in the same folder

the code:

import numpy as np import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense from tensorflow.keras.optimizers import Adam from keras.utils.np_utils import to_categorical from keras.layers import Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D import cv2 from sklearn.model_selection import train_test_split import pickle import os import pandas as pd import random from keras.preprocessing.image import ImageDataGenerator

########### Parameters

path = “myData” # folder with all the class folders labelFile = ‘labels.csv’ # file with all names of classes batch_size_val = 50 # how many to process together steps_per_epoch_val = 2000 epochs_val = 10 imageDimesions = (32, 32, 3) testRatio = 0.2 # if 1000 images split will 200 for testing validationRatio = 0.2 # if 1000 images 20% of remaining 800 will be 160 for validation

######################### Importing of the Images

count = 0 images = [] classNo = [] myList = os.listdir(path) print(“Total Classes Detected:”, len(myList)) noOfClasses = len(myList) print(“Importing Classes…..”) for x in range(0, len(myList)): myPicList = os.listdir(path + “/” + str(count)) for y in myPicList: curImg = cv2.imread(path + “/” + str(count) + “/” + y) images.append(curImg) classNo.append(count) print(count, end=” “) count += 1 print(” “) images = np.array(images) classNo = np.array(classNo)

######################### Split Data

X_train, X_test, y_train, y_test = train_test_split(images, classNo, test_size=testRatio) X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validationRatio)

X_train = ARRAY OF IMAGES TO TRAIN y_train = CORRESPONDING CLASS ID ######################### TO CHECK IF NUMBER OF IMAGES MATCHES TO NUMBER OF LABELS FOR EACH DATA SET

print(“Data Shapes”) print(“Train”, end=””); print(X_train.shape, y_train.shape) print(“Validation”, end=””); print(X_validation.shape, y_validation.shape) print(“Test”, end=””); print(X_test.shape, y_test.shape) assert (X_train.shape[0] == y_train.shape[ 0]), “The number of images in not equal to the number of lables in training set” assert (X_validation.shape[0] == y_validation.shape[ 0]), “The number of images in not equal to the number of lables in validation set” assert (X_test.shape[0] == y_test.shape[0]), “The number of images in not equal to the number of lables in test set” assert (X_train.shape[1:] == (imageDimesions)), ” The dimesions of the Training images are wrong ” assert (X_validation.shape[1:] == (imageDimesions)), ” The dimesionas of the Validation images are wrong ” assert (X_test.shape[1:] == (imageDimesions)), ” The dimesionas of the Test images are wrong”

######################### READ CSV FILE

data = pd.read_csv(labelFile) print(“data shape “, data.shape, type(data))

######################### DISPLAY SOME SAMPLES IMAGES OF ALL THE CLASSES

num_of_samples = [] cols = 5 num_classes = noOfClasses fig, axs = plt.subplots(nrows=num_classes, ncols=cols, figsize=(5, 300)) fig.tight_layout() for i in range(cols): for j, row in data.iterrows(): x_selected = X_train[y_train == j] axs[j][i].imshow(x_selected[random.randint(0, len(x_selected) – 1), :, :], cmap=plt.get_cmap(“gray”)) axs[j][i].axis(“off”) if i == 2: axs[j][i].set_title(str(j) + “-” + row[“Name”]) num_of_samples.append(len(x_selected))

######################### DISPLAY A BAR CHART SHOWING NO OF SAMPLES FOR EACH CATEGORY

print(num_of_samples) plt.figure(figsize=(12, 4)) plt.bar(range(0, num_classes), num_of_samples) plt.title(“Distribution of the training dataset”) plt.xlabel(“Class number”) plt.ylabel(“Number of images”) plt.show()

######################### PREPROCESSING THE IMAGES

def grayscale(img): img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) return img

def equalize(img): img = cv2.equalizeHist(img) return img

def preprocessing(img): img = grayscale(img) # CONVERT TO GRAYSCALE img = equalize(img) # STANDARDIZE THE LIGHTING IN AN IMAGE img = img / 255 # TO NORMALIZE VALUES BETWEEN 0 AND 1 INSTEAD OF 0 TO 255 return img

X_train = np.array(list(map(preprocessing, X_train))) # TO IRETATE AND PREPROCESS ALL IMAGES X_validation = np.array(list(map(preprocessing, X_validation))) X_test = np.array(list(map(preprocessing, X_test))) cv2.imshow(“GrayScale Images”, X_train[random.randint(0, len(X_train) – 1)]) # TO CHECK IF THE TRAINING IS DONE PROPERLY

######################### ADD A DEPTH OF 1

X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1) X_validation = X_validation.reshape(X_validation.shape[0], X_validation.shape[1], X_validation.shape[2], 1) X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)

######################### AUGMENTATAION OF IMAGES: TO MAKEIT MORE GENERIC

dataGen = ImageDataGenerator(width_shift_range=0.1, # 0.1 = 10% IF MORE THAN 1 E.G 10 THEN IT REFFERS TO NO. OF PIXELS EG 10 PIXELS height_shift_range=0.1, zoom_range=0.2, # 0.2 MEANS CAN GO FROM 0.8 TO 1.2 shear_range=0.1, # MAGNITUDE OF SHEAR ANGLE rotation_range=10) # DEGREES dataGen.fit(X_train) batches = dataGen.flow(X_train, y_train, batch_size=20) # REQUESTING DATA GENRATOR TO GENERATE IMAGES BATCH SIZE = NO. OF IMAGES CREAED EACH TIME ITS CALLED X_batch, y_batch = next(batches)

TO SHOW AGMENTED IMAGE SAMPLES

fig, axs = plt.subplots(1, 15, figsize=(20, 5)) fig.tight_layout()

the error: myPicList = os.listdir(path + “/” + str(count)) FileNotFoundError: [WinError 3] The system cannot find the path specified: ‘myData/0’

Advice for someone who’s gonna take the Tensorflow exam ? Thaaanks

Hi Tensorflow Experts,

​

I have been working on two codes, one in TF1, the other one in TF2. I did some research about the TensorFlow architecture the difference between these two, maybe you could check if my understanding is correct?

In version 1, graphs need to be created manually by the user. In version 2, the API has been made more user-friendly and the graph creation is now automated in Keras. Has Keras been created explicitly for TensorFlow 2, or does it exist independently from it?

​

The eager execution mode basically breaks the graph approach to create a more “classical” computation scheme. It is used per default in pure high-level TensorFlow, since here the computations have a smaller impact on performance. On the other hand, in Keras, eager mode is switched off and behaves more or less like TF1. Is this correct?

I have extracted audio embeddings from Google Audioset corpus.
Now, I want to use these audio embeddings for training my own model (CNN). I have some confusion about these audio embeddings.

1. Should I extract STFT and MFCC from the audio embeddings? If so, how can I do that (any way to use librosa?)? Or, are the audio embeddings already transformed to MFCC?
2. What should be the best way to split the audio set corpus into train, test and validate datasets? They are if Tfrecord format and each tfrecord file contain various segment of audio clips having different class labels.
3. If I want to work on selective class labels (such as rowing, or car sound), what should be the best way to extract the selective audio segments?

Also, please share some helpful resources about working with Google audioset corpus if possible.