Develop Reference

Problem in generating Grad-Cam maps for input images

I am using the following code to generate the GradCam maps for the input images. I am not able to resize the subplot to visualise them properly as soon in the figure below.
Code:
import cv2
def NormalizeData(data):
return (data - np.min(data)) / (np.max(data) - np.min(data))
for i in range(5):
img = train_loader.dataset[i][0].to(device)
mdlOut = model(img)
#img = torch.tensor(img, requires_grad=True)
img=img.clone().detach().requires_grad_(True) #newline
out = model(img)
idx = out.argmax()
yc = out[0,idx]
yc.backward()
rawGrad = img.grad.cpu().detach().numpy()
grad = NormalizeData(rawGrad)
avgGrad = np.mean(np.abs(grad),axis=(1,2))
img = NormalizeData(img.cpu().detach().numpy())
CAM = NormalizeData(avgGrad[0]*img[0,:,:] \+ avgGrad[1]*img[1,:,:] \+ avgGrad[2]*img[2,:,:])
plt.subplot(20,2,2*i+1)
plt.imshow(CAM,cmap='jet')
plt.subplot(20,2,2*i+2)
plt.imshow(np.einsum('kli->lik',img))

I'm having trouble with pasting a gif on an image using PIL

from PIL import Image, ImageSequence
import PIL
GIF_PATH = Image.open(r"C:\Users\me_\My\Filw\Path.gif")
IMAGE_PATH = Image.open(r"base.png")
frames = []
for frame in ImageSequence.Iterator(GIF_PATH):
output = IMAGE_PATH.copy()
frame_px = frame.load()
output_px = output.load()
transparent_foreground = frame.convert('RGBA')
transparent_foreground_px = transparent_foreground.load()
for x in range(frame.width):
for y in range(frame.height):
if frame_px[x, y] in (frame.info["background"], frame.info["transparency"]):
continue
output_px[x, y] = transparent_foreground_px[x, y]
output =output.resize([436,249], PIL.Image.NEAREST)
frames.append(output)
frames[0].save('output.gif',save_all = True, append_images = frames[1:], optimize = False, duration = 40, loop=0)
how would I paste the image to a specific location?
I'm pretty new so trying to get a grasp
I tried using imagechops offset but the image just wrapped around instead of moving

Deep Learning U-net keras patch making from source image.(code python)

I have problem with preprocessing data set for deep learning.
I am using U-net.
I have training data, label data, test data size of 512x512.
I want patch based learning so i am trying to change 512x512 slices to multiple 64x64 slices so that I can train image with 64x64 patches. In my case I want to make 64x64 patches stride 32 pixel from original 512x512 Images.
For example, first patch is formed from (0,0) ~ (64,64) and the next patch is formed from (32,0) ~ (64 + 32, 64)
This is full code of data.py
#from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import numpy as np
import os
import glob
import cv2
from os.path import *
#from libtiff import TIFF
import matplotlib.pyplot as plt
class dataProcess(object):
def __init__(self, out_rows, out_cols):
"""
"""
self.out_rows = out_rows
self.out_cols = out_cols
def load_train_data(self):
imgs_row, imgs_col = 512,512
train_list = []
train_img = []
label_list = []
label_img = []
train_path = 'C:\\Users\\Lee Doyle\\unet\\data\\Train'
label_path = 'C:\\Users\\Lee Doyle\\unet\\data\\Label'
######################Traindata################################
print('-' * 30)
print('load train images...')
print('-' * 30)
for i in glob.glob(train_path + '/*.[tT][iI][fF]'):
train_list.append(abspath(i))
print(len(train_list))
for i in train_list:
# print(i)
img = cv2.imread(i, cv2.IMREAD_GRAYSCALE)
train_img.append(img.astype(np.float32)/255.0)
train_img = np.array(train_img)
print(train_img)
######################Labeldata################################
for i in glob.glob(label_path + '/*.[tT][iI][fF]'):
label_list.append(abspath(i))
for i in label_list:
img = cv2.imread(i, cv2.IMREAD_GRAYSCALE)
label_img.append(img.astype(np.float32) / 255.0)
label_img = np.array(label_img)
fig = plt.figure()
a=fig.add_subplot(1,2,1)
plt.imshow(train_img[0],cmap='gray')
a.set_title('trian image')
a=fig.add_subplot(1,2,2)
plt.imshow(label_img[0]+train_img[0],cmap='gray')
plt.imshow(label_img[0],cmap='gray')
a.set_title('Image with GT SEG ')
plt.show()
print(train_img.shape)
train_img = train_img.reshape(train_img.shape[0], imgs_row, imgs_col, 1)
label_img = label_img.reshape(label_img.shape[0], imgs_row, imgs_col, 1)
return train_img,label_img
def load_test_data(self):
imgs_row, imgs_col = 512,512
test_list = []
test_img = []
test_path = 'C:\\Users\\Lee Doyle\\unet\\data\\Test'
######################Testdata#################################
print('-' * 30)
print('load test images...')
print('-' * 30)
for i in glob.glob(test_path + '/*.[tT][iI][fF]'):
test_list.append(abspath(i))
for i in test_list:
img = cv2.imread(i, cv2.IMREAD_GRAYSCALE)
# img=cv2.resize(img,(512,512))
test_img.append(img.astype(np.float32) / 255.0)
test_img = np.array(test_img)
# mean = test_img.mean(axis=0)
# test_img -= mean
test_img=test_img.reshape(test_img.shape[0],imgs_row,imgs_col,1)
print(test_img.shape)
return test_img
if __name__ == "__main__":
mydata = dataProcess(512,512)
mydata.load_train_data()
mydata.load_test_data()
I think I need to add patch making code near this code.
for i in glob.glob(train_path + '/*.[tT][iI][fF]'):
train_list.append(abspath(i))
print(len(train_list))
for i in train_list:
# print(i)
img = cv2.imread(i, cv2.IMREAD_GRAYSCALE)
train_img.append(img.astype(np.float32)/255.0)
train_img = np.array(train_img)
print(train_img)
I apppreciate your help.

why no image is displayed on histogram equalization : python

import numpy as np
import cv2
import scipy.misc, math
from scipy.misc.pilutil import Image
while True:
img = Image.open('img.jpg').convert(L)
img1 = scipy.misc.fromimage(img)
f1 = img1.flatten()
hist,bins = np.histogram(img1, 256,[0,255])
cdf = hist.cumsum
cdf_m = np.ma.masked_equal(cdf,0)
cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_m,0)
im2 = cdf[f1]
im3 = np.reshape(im2,img1.shape)
im4 = scipy.misc.toimage(img3)
im4.show()
I am trying to histogram equalize an image. When i run the program, neither an output image is displayed nor an error is shown.
Can anyone tell me where am going wrong

Try:
import numpy as np
import cv2
import scipy.misc, math
from scipy.misc.pilutil import Image
while True:
img = Image.open('img.jpg').convert(L)
img1 = scipy.misc.fromimage(img)
f1 = img1.flatten()
hist,bins = np.histogram(img1, 256,[0,255])
cdf = hist.cumsum
cdf_m = np.ma.masked_equal(cdf,0)
cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_m,0)
im2 = cdf[f1]
im3 = np.reshape(im2,img1.shape)
im4 = scipy.misc.toimage(im3)
im4.format = "PNG" # out of paranoia mainly
im4.show()

Corrupted image being saved by cv.SaveImage() in opencv

import sys, Image, scipy, cv2, numpy
from scipy.misc import imread
from cv2 import cv
from SRM import SRM
def ndarrayToIplImage (source):
"""Conversion of ndarray to iplimage"""
image = cv.CreateImageHeader((source.shape[1], source.shape[0]), cv.IPL_DEPTH_8U, 3)
cv.SetData(image, source.tostring(), source.dtype.itemsize * 3 * source.shape[1])
return image
"""Main Program"""
filename = "snap.jpeg"
Q = 64
im = imread(filename)
name = filename[:-4]
img = Image.fromarray(im)
if img.size[0] > 200 or img.size[1] > 200:
ratio = img.size[0]/img.size[1]
size = int(ratio*200), 200
img = numpy.array(img.resize(size, Image.ANTIALIAS))
srm = SRM(img, Q)
srm.initialization()
srm.segmentation()
classes, map = srm.map()
"""Converting ndarray to PIL Image to iplimage"""
pil_img = Image.fromarray(map)
cv_img = cv.CreateImageHeader(pil_img.size, cv.IPL_DEPTH_8U, 3)
cv.SetData(cv_img, pil_img.tostring(), pil_img.size[0]*3)
print type(cv_img) ##prints <type 'cv2.cv.iplimage'>
"""Using ndarrayToIplImage function also gives the same error!"""
"""
cv_img if of type iplimage but still gives error while using cv.ShowImage()
or cv.SaveImage().
There is no error displayed. Just the console hangs...
"""
I am using the SRM (Statistical Region Merging) Package available at this page.
I have just changed the example program given in the package. I had to convert the type returned by the SRM package functions to iplimage. There is no error in using the package but somewhere in using opencv functions.
This is the image that is saved after the console closes after hanging.
It used cv.SaveImage().
I tried cv2.imwrite() and I got this as the result:
This is the image that should have been saved. I used scipy.misc.imsave('image.jpg', map) to save this.

Why do you use IplImage and PIL? SRM library read numpy array and you get a numpy array from cv2.imread(image), then if you need to resize yuor image you can use opencv function cv2.resize(...). Finally you can save an image with opencv with cv2.imwrite(...) your code should appear like this:
import sys, cv2, numpy
from SRM import SRM
"""Main Program"""
filename = "snap.jpeg"
Q = 64
img = cv2.imread(filename)
name = filename[:-4]
if img.shape[0] > 200 or img.shape[1] > 200:
ratio = img.shape[0] * 1. / img.shape[1]
size = (int(ratio * 200), 200)
img = cv2.resize(img, size, interpolation=cv2.INTER_LANCZOS4)
srm = SRM(img, Q)
srm.initialization()
srm.segmentation()
classes, srmMap = srm.map() # Map is a python function, use different variable name
srmMap = srmMap.astype('uint8') # or you can try other opencv supported type
# I suppose that srmMap is your image returned as numpy array
cv2.imwrite('name.jpeg', srmMap)
# or
cv2.imshow('image', srmMap)
cv2.waitKey(0)