Develop Reference

How to change tensor image dimensions with a manually defined class in Tensorflow?

I have a manually defined class to load images in Tensorflow 2.
class MasterImage0(object):
def __init__(self,PATH='', IMAGE_SIZE = 128):
self.PATH = PATH
self.IMAGE_SIZE = IMAGE_SIZE
self.image_data = []
self.x_data = []
self.y_data = []
self.CATEGORIES = []
# This will get List of categories
self.list_categories = []
def get_categories(self):
for path in os.listdir(self.PATH):
if '.DS_Store' in path:
pass
else:
self.list_categories.append(path)
print("Found Categories ",self.list_categories,'\n')
return self.list_categories
def process_image(self):
try:
"""
Return Numpy array of image
:return: X_Data, Y_Data
"""
self.CATEGORIES = self.get_categories()
for categories in self.CATEGORIES: # Iterate over categories
train_folder_path = os.path.join(self.PATH, categories) # Folder Path
class_index = self.CATEGORIES.index(categories) # this will get index for classification
for img in os.listdir(train_folder_path): # This will iterate in the Folder
new_path = os.path.join(train_folder_path, img) # image Path
try: # if any image is corrupted
image_data_temp = cv2.imread(new_path) # Read Image as numbers
image_temp_resize = cv2.resize(image_data_temp,(self.IMAGE_SIZE,self.IMAGE_SIZE))
self.image_data.append([image_temp_resize,class_index])
random.shuffle(self.image_data)
except:
pass
data = np.asanyarray(self.image_data)
# Iterate over the Data
for x in data:
self.x_data.append(x[0]) # Get the X_Data
self.y_data.append(x[1]) # get the label
X_Data = np.asarray(self.x_data) / (255.0) # Normalize Data
Y_Data = np.asarray(self.y_data)
# reshape x_Data
X_Data = X_Data.reshape(-1, self.IMAGE_SIZE, self.IMAGE_SIZE, 3)
print("Compiled X_data", self.X_data)
return X_Data, Y_Data
except:
print("Failed to run Function Process Image ")
def pickle_image(self):
"""
:return: None Creates a Pickle Object of DataSet
"""
# Call the Function and Get the Data
X_Data,Y_Data = self.process_image()
# Write the Entire Data into a Pickle File
pickle_out = open('X_Data','wb')
pickle.dump(X_Data, pickle_out)
pickle_out.close()
# Write the Y Label Data
pickle_out = open('Y_Data', 'wb')
pickle.dump(Y_Data, pickle_out)
pickle_out.close()
print("Pickled Image Successfully ")
return X_Data,Y_Data
print(self.X_Data)
def load_dataset(self):
try:
# Read the Data from Pickle Object
X_Temp = open('..\SeekThermal\X_Data','rb')
X_Data = pickle.load(X_Temp)
Y_Temp = open('..\SeekThermal\Y_Data','rb')
Y_Data = pickle.load(Y_Temp)
print('Reading Dataset from Pickle Object')
return X_Data,Y_Data
except:
print('Could not Found Pickle File ')
print('Loading File and Dataset ..........')
X_Data,Y_Data = self.pickle_image()
print("X_data", self.X_Data)
return X_Data,Y_Data
So I can load my training images like this and the shape of my tensor is 100*100*3.
a = MasterImage0(PATH = path_train,IMAGE_SIZE = 128)
#a.load_dataset()
(train_images, train_labels) = a.load_dataset()
print(train_images.shape
Out: (4732, 100, 100, 3)
The original image size is 300x400 pixels. I do not understand why I cannot change the the tensor shape to 128 instead of 100, for example. I thought I could do it by setting IMAGE_SIZE = 128?
Where does it take the 100 from? thank you.

cv2.resize() and np.array.reshape() must work correctly, if they did not work, they would throw exception, but obviously, no exception was thrown here, only just your setting IMAGE_SIZE=128 did not work.
So, here the problem is most likely caused by using pickle, which cached your codes' buffer (all resized and reshaped images) at the first time and reuesd it again when you re-run your codes.
If so, I suspect that you must have set IMAGE_SIZE=100 once, cached all (*,100,100,3) results, and here when you run by setting IMAGE_SIZE=128, the cached(pickled) file ((*,100,100,3) size files) will just be returned, leading to this problem.
You can delete all cached files in ..\SeekThermal and try again
or
rewrite try part of func load_dataset like the following to give each cached file a stamp
...
try:
# Read the Data from Pickle Object
X_Temp = open(f'..\SeekThermal\X_Data_{self.IMAGE_SIZE}','rb')
X_Data = pickle.load(X_Temp)
Y_Temp = open(f'..\SeekThermal\Y_Data_{self.IMAGE_SIZE}','rb')
Y_Data = pickle.load(Y_Temp)
print('Reading Dataset from Pickle Object')
return X_Data,Y_Data
...

Running tensorflow on server Python

trying to use tensorflow on ipage server with operating system centOs 7
I don't know if there is a GPU or not , but I get this error message
kernel driver does not appear to be running on this host /proc/driver/nvidia/version does not exist
I tried this code
from tensorflow import config
config.set_soft_device_placement = True
and also this code trying to prevent tensorflow from using GPU
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
but I kept receiving the same error message
Note :
I am trying to run face recognition script on server to be used by php through terminal using exec() function by passing the image as string base64 and then decoding it and then adding new face or fetching faces and comparing and then will be saved in the database
and this is my script :
import face_recognition as fr
from tensorflow.keras.models import model_from_json
import numpy as np
from PIL import Image
import base64
import io
import json
import cv2
from tensorflow import config
config.set_soft_device_placement = True
def check_if_spoof(image_string_base64):
# decoding the image
msg = base64.b64decode(image_string_base64)
buf = io.BytesIO(msg)
img = Image.open(buf).convert("RGB")
opencv_image = np.array(img)
test_image = opencv_image[:,:,::-1].copy()
json_file = open('antispoofing_models/antispoofing_model.json','r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load antispoofing model weights
model.load_weights('antispoofing_models/antispoofing_model.h5')
resized_face = cv2.resize(test_image,(160,160))
resized_face = resized_face.astype("float") / 255.0
# resized_face = img_to_array(resized_face)
resized_face = np.expand_dims(resized_face, axis=0)
# pass the face ROI through the trained liveness detector
# model to determine if the face is "real" or "fake"
preds = model(resized_face)[0]
if preds> 0.5:
return True
else:
return False
def decode_img(image_string_base64):
msg = base64.b64decode(image_string_base64)
buf = io.BytesIO(msg)
img = Image.open(buf)
return img
def resize_image(image_string_base64, height=500):
image = decode_img(image_string_base64)
height_percent = (height / float(image.size[1]))
width_size = int((float(image.size[0]) * float(height_percent)))
image = image.resize((width_size, height), Image.NEAREST)
return np.array(image)
def add_person(image_string_base64):
if check_if_spoof(image_string_base64):
return {'error':'image is fake'}
img = np.array(decode_img(image_string_base64))
img_encodings = fr.face_encodings(img, num_jitters=4)
return np.array(img_encodings)
def get_person(image_string_base64, data, tolerance=0.35):
if check_if_spoof(image_string_base64):
return {'error':'image is fake'}
data = json.loads(data)
resized_img = resize_image(image_string_base64)
img_encoding = fr.face_encodings(resized_img)
############## testing purpose only #########
# return fr.compare_faces(data,img_encoding)
#############################################
# type of recieved data is : { id ->number : encoding->list}
for user in data:
for id_ , encoding in user:
if True in fr.compare_faces(encoding, img_encoding):
return id_
return {'error':"user not existed"}

(error: (-215:Assertion failed) total(os[i]) > 0 in function 'getLayerShapesRecursively') when upsampling image

I would like to improve the resolution of my .PNG file using EDSR method.
However, while running my code :
'''
import cv2
import os
def load_images_from_folder(folder):
images = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder,filename))
if img is not None:
images.append(img)
return images
img = load_images_from_folder('imgSource')
sr = cv2.dnn_superres.DnnSuperResImpl_create()
path = "imgModel/EDSR_x4.pb"
sr.readModel(path)
sr.setModel("edsr",4)
result = []
for i in range(0, len(img)):
imgRes = sr.upsample(img[i])
result.append(imgRes)
'''
On the line :
imgRes = sr.upsample(img[i])
It shown error like this :
error: OpenCV(4.5.3) /tmp/pip-req-build-tjxnaiom/opencv/modules/dnn/src/dnn.cpp:3534: error: (-215:Assertion failed) total(os[i]) > 0 in function 'getLayerShapesRecursively'
Is there any parts that I have to change? Thank you :)

AttributeError: 'decode' when reading TIFF images

Here is part of the code I am attempting to run:
import numpy as np
import os
import tensorflow as tf
import imageio
import sys
#Create tensorflowflow dataset
dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels))
if not is_test:
dataset = dataset.shuffle(num_of_samples)
dataset = dataset.repeat(None)
dataset = dataset.map(self._parse_dataset)
if not is_test:
batched_dataset = dataset.batch(self.batch_size, drop_remainder=True).prefetch(20)
else:
batched_dataset = dataset.batch(self.test_batch_size)
#Create the iterator
return batched_dataset, num_of_samples, path_strings
def get_batch(self, subset="train"):
batch_of_images = self.iterators[subset].get_next()
return batch_of_images
def _read_tif(self, file_path):
file_path = file_path.decode(sys.getdefaultencoding())
try :
im = imageio.imread(file_path)
except:
im = np.zeros((self.width, self.height, 3))
if len(im.shape) != 3:
im = np.repeat(im[:, :, np.newaxis], 3, axis=2)
return im
def _read_image(self, file_path):
return tf.py_function(func=self._read_tif, inp=[file_path], Tout=tf.uint8)
and I have the following error coming up:
File "C:\PROJECTS_RUNNING2\pipeline\data_loader\data_generator.py", line 131, in _read_tif
file_path = file_path.decode(sys.getdefaultencoding())
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'decode'
The file_path is defined in the run.py and looks like this:
def main(config_file_path):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
config =tf.ConfigProto(gpu_options=gpu_options)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
tf.reset_default_graph()
config = parse_config_file(config_file_path)
#Create the experiment output folders, this is where the outputs will be saved
output_folder_path = config["output_path"]
output_path = create_output_folder(output_folder_path, config["experiment_name"])
copyfile(config_file_path, os.path.join(output_path, "%s_parameters.json" % config["experiment_name"]))
data_generator = DataGenerator(config)
Input and Output dataset file paths are correctly defined in the config file.
I a very much beginner in coding though have to use the script for analysis of my images and I am struggling to get it up and running. Im using Python 3.7 and Tensorflow 1.14. Any help to resolve this error will be really much appreciated!

face recognition can't detect if image face is not on portrait

I try using face recognition from this link: face recognition
then modif the code like this, main.py:
#!/usr/bin/env python
import cgitb, cgi
cgitb.enable()
print("Content-Type: text/html;charset=utf-8")
print "Content-type:text/html\r\n\r\n"
import base64
import simplejson as json
import re
import face_recognition
import numpy as np
import io
from imageio import imread
from PIL import Image
import datetime
import os, errno
import shutil
params = cgi.FieldStorage()
now = datetime.datetime.now()
date = str(now)
date2 = date.replace(" ","")
img = params.getvalue('img')
data1 = json.loads(img)
data2 = data1['img2']['data']
numparray = data1['img1']
numparray2 = numparray.replace(" ", "+")
b=bytes(numparray2)
imgdata = base64.b64decode(b)
os.makedirs(date2)
with open(date2+"/img1.png", "wb") as f:
f.write(imgdata)
image = face_recognition.load_image_file(date2+'/img1.png')
try:
face_encode = face_recognition.face_encodings(image)[0]
#print("face_encode = ".format(face_encode))
except IndexError:
print("encode image failed")
quit()
known_faces = []
y = 1
for images in data2:
ir = images.replace(" ", "+")
ib = bytes(ir)
imagedata = base64.b64decode(ib)
x = str(y)
with open(date2+"/compare"+x+".png", "wb") as g:
g.write(imagedata)
compare = face_recognition.load_image_file(date2+"/compare"+x+".png")
try:
compare_encode = face_recognition.face_encodings(compare)[0]
#print("face_encode = ".format(face_encode))
except IndexError:
print("encode image compare failed")
quit()
known_faces.append(compare_encode)
y = y+1
results = face_recognition.face_distance(known_faces, face_encode)
datahasil = []
#hasilakhir = "{"
for i, face_distance in enumerate(results):
h = "{:.2}".format(face_distance, i)
#hasilakhir = hasilakhir+"compare{}"
datahasil.append(h)
hasilakhir = ','.join(datahasil)
shutil.rmtree(date2, ignore_errors=True)
print("{\"hasilcompare\" : \"" +hasilakhir+ "\"}")
the final result is compare between 2 image and give the score, in case photo of image is potrait compare is successfull, but when one of image is not on potrait (face not on potrait potision) or like face angle more than 90 degree, that give error message in catch encode failed..
i have try another way with face detection before sending 2 image to main.py to detect the image in face but when it can't detect faces i try to rotate the image untill the code detect face, but sometimes face detection can detect face with angle 90degree but in face recognition(main.py) still can't read the face.
code of rotate is here, rotate.py:import numpy as np
import cv2
from scipy import ndimage, misc
import os
from PIL import Image
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
for counter in range (0, 4):
img = cv2.imread('img/1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
if(len(faces) == 1):
i = False
print ("face found")
break
else:
print("no face found")
i = False
img = Image.open("img/1.jpg")
img.rotate(90).save("img/1.jpg")

Yes, I think this is the problem with the library. I tested it on the same image. Once after rotation and once on the original image. It does not detect in the rotated image, both the face_locations and face_encodings are empty lists.