def graficos():
'''
Con esta funcion, importamos graficos agrupados por generaciones y los imprimimos.
'''
from PIL import Image
data = opc.cleaning('input/pokemon.csv')
print(
f'Por si te interesa, los datos estadisticos agrupados por generaciones son: '
)
total_gen = data.groupby("Generation").agg({
"Attack": "mean",
"Defense": "mean",
}).plot.line()
plt.title('Media de ataque y defensa por generacion')
plt.savefig("output/Meanattack.jpg", bbox_inches='tight')
max_gen = data.groupby("Generation").agg({
"Attack": "max",
"Defense": "max",
}).plot.bar()
plt.title('Ataques y defensas mas altos de cada generación')
plt.savefig("output/Maxattack.jpg", bbox_inches='tight')
min_gen = data.groupby("Generation").agg({
"Attack": "min",
"Defense": "min",
}).plot.barh()
plt.title('Ataques y defensas mas bajos de cada generacion')
plt.savefig("output/Minattack.jpg", bbox_inches='tight')
img = Image.open('output/Meanattack.jpg')
img.show()
img2 = Image.open('output/Minattack.jpg')
img2.show()
img3 = Image.open('output/Maxattack.jpg')
img3.show()
def __getitem__(self, idx):
"""
returns image data & target for the corresponding index
"""
try:
bg_image = Image.open(self.bg_images[idx]).convert('L') # BG Images
bgfg_image = Image.open(self.bgfg_images[idx]).convert('L') # BG_FG Images
mask_image = Image.open(self.mask_images[idx]).convert('L') # Mask Images
depth_map = Image.open(self.depth_maps[idx]).convert('L') # Depth Images
## Transformed Images
bg_image = self.bg_transforms(bg_image)
bgfg_image = self.bgfg_transforms(bgfg_image)
mask_image = self.mask_transforms(mask_image) # first transform the image
depth_map = self.depthmap_transforms(depth_map)
# upd_mask = mask_image.unsqueeze(0) # then add one channel by unsqueezing it
# print('shape of mask_image', upd_mask.size())
return {"bg": bg_image, "bgfg": bgfg_image, "mask": mask_image, "dpmap": depth_map} # dict way of returning Depth Map
# also add "bg": bg_image,
except Exception as e:
print("Image {0} skipped due to {1}".format(self.bgfg_images[idx],e)) # this is only if some images can't be identified by PIL
def display_image(url):
"""Downloads an image from a URL and displays it in Databricks."""
filename = url.split('/')[-1]
filepath = os.path.join(model_dir, filename)
urllib.request.urlretrieve(url, filepath)
image = os.path.join(model_dir, filename)
image_png = image.replace('.jpg','.png')
Image.open(image).save(image_png,'PNG')
img = mpimg.imread(image_png)
plt.imshow(img)
display()
def process_images(folder):
classes = [os.path.join(folder, d) for d in sorted(os.listdir(folder))] # get list of all sub-folders in folder
img_cnt = 0
for class_x in classes:
if os.path.isdir(class_x):
# get paths to all the images in this folder
images = [os.path.join(class_x, i) for i in sorted(os.listdir(class_x)) if i != '.DS_Store']
for image in images:
img_cnt = img_cnt + 1
if(img_cnt % 1000 == 0): # show progress
print("Processed %s images" % str(img_cnt))
im = Image.open(image)
im = im.resize(dimensions) # resize image according to dimensions set
im = make_greyscale_white_bg(im, 127, 127, 127) # turn grey background (if any) to white, and
# convert into greyscale image with 1 channel
im = invert_colors(im)
im.save(image) # overwrite previous image file with new image
print("Finished processing images, images found = ")
print(img_cnt)
def gen_data2(self,input_byte,input_path):
img = Image.open(input_byte)
img = img.resize((self.input_shape[0],self.input_shape[1]))
img = self.pad_and_bg(img)
blur_rad = np.random.normal(loc=0.0, scale=2, size=None)
img = img.filter(ImageFilter.GaussianBlur(blur_rad))
enhancer_contrat = ImageEnhance.Contrast(img)
enhancer_color = ImageEnhance.Color(img)
enhancer_brightness = ImageEnhance.Brightness(img)
contrast_factor = np.random.normal(loc=1.0, scale=0.5, size=None)
brightness_factor = np.random.normal(loc=1.0, scale=0.5, size=None)
color_factor = np.max([0,1-abs(np.random.normal(loc=0, scale=0.5, size=None))])
img = enhancer_contrat.enhance(contrast_factor)
img = enhancer_brightness.enhance(brightness_factor)
img = enhancer_color.enhance(color_factor)
# img.save('test_blur.png')
img = np.asarray(img)/127.5-1
t = os.path.dirname(input_path)[-1:]
# print(t,input_path)
switcher = {
'1': [1,0,0,0,0,0,0],
'2': [0,1,0,0,0,0,0],
'3': [0,0,1,0,0,0,0],
'4': [0,0,0,1,0,0,0],
'5': [0,0,0,0,1,0,0],
'6': [0,0,0,0,0,1,0],
'0': [0,0,0,0,0,0,1],
}
target = switcher.get(t)
# print(t)
return img,target
def nbread(imagefile):
"""
- Purpose
Read an image from a file to a numpy array.
- Synopsis
arr = nbread(imagefile)
- Input
imagefile: Image file path.
- Output
arr: numpy array representing an image.
"""
image_path = findImageFile(imagefile)
with Image.open(image_path) as image:
im_arr = np.fromstring(image.tobytes(), dtype=np.uint8)
bands = 3
if image.mode == 'RGBA':
im_arr = im_arr.reshape((image.size[1], image.size[0], 4))
elif image.mode == 'L':
im_arr = im_arr.reshape((image.size[1], image.size[0]))
elif image.mode == 'RGB':
im_arr = im_arr.reshape((image.size[1], image.size[0], 3))
elif image.mode == '1':
print('does not support bit mode')
return None
else:
im_arr = im_arr.reshape((image.size[1], image.size[0], 3))
return im_arr
def process_images(folder):
classes = [os.path.join(folder, d) for d in sorted(os.listdir(folder))
] # get list of all sub-folders in folder
img_cnt = 0
for class_x in classes:
if os.path.isdir(class_x):
# get paths to all the images in this folder
images = [
os.path.join(class_x, i) for i in sorted(os.listdir(class_x))
if i != '.DS_Store'
]
for image in images:
img_cnt = img_cnt + 1
if (img_cnt % 1000 == 0):
print("Processed %s images" % str(img_cnt))
im = Image.open(image)
im = im.resize(
dimensions) # resize image according to dimensions set
im.save(image) # overwrite previous image file with new image
print("Finished processing images, images found = ")
print(img_cnt)
def rotate_image(filepath):
'''Phones rotate images by changing exif data,
but we really need to rotate them for processing'''
image = Image.open(filepath)
try:
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == 'Orientation':
break
exif = dict(image._getexif().items())
if exif[orientation] == 3:
print('ROTATING 180')
image = image.rotate(180, expand=True)
elif exif[orientation] == 6:
print('ROTATING 270')
image = image.rotate(270, expand=True)
elif exif[orientation] == 8:
print('ROTATING 90')
image = image.rotate(90, expand=True)
image.save(filepath)
image.close()
except (AttributeError, KeyError, IndexError):
# cases: image don't have getexif
pass
return (image)
def test(self):
label = {
0: '1',
1: '2',
2: '3',
3: '4',
4: '5',
5: 'x',
6: '0',
}
i = np.random.randint(0,len(self.pathlisttest))
input_img = Image.open(self.pathlisttest[i])
transformed_pil,plot_pil,cropped_data = self.CamScan.cropAll(input_img,10)
input_shape = self.input_shape
batch_data = np.zeros((len(cropped_data),input_shape[0],input_shape[1],input_shape[2]))
for index in range(len(cropped_data)):
batch_data[index,:,:,:]=np.asarray(cropped_data[index]['image'].resize((self.input_shape[0],self.input_shape[1])))
batch_data = (batch_data/127.5)-1
result = self.model.predict(batch_data)
result = np.argmax(result,axis=1)
result2 = []
for index in range(len(cropped_data)):
result2.append(label[result[index]])
final_img = self.CamScan.plot_result(plot_pil,result2)
final_img.save(r'images/output_test/'+'test.jpg')
def directDownload(url):
request = rlib.Request(url)
response = rlib.urlopen(request)
data = response.read()
args = []
split = url.split('.')
args.append(split[-1])
print("Image type: " + args[0])
img = Image.open(io.BytesIO(data))
size = img.size
print("Image size: " + str(size))
fname = "temp." + args[0]
#resize image if needed
if (size[0] > 1500 or size[1] > 1500):
print("resizing img")
max = np.maximum(size[0], size[1])
divisor = float(max // 1500)
newWidth = int(size[0] / divisor)
newHeight = int(size[1] / divisor)
img = img.resize((newWidth, newHeight), Image.ANTIALIAS)
print("resized")
try:
img.save(fname)
print("Saved input picure as temp." + args[0])
except:
print("Failed to save image")
args[0] = "fail"
return args
def visualise_baseline_mix(LL, X, mask, t=0, figsize=(10, 10), filename=None):
# Unpack X_tensor
lon = X[:, t, :mask[t], 0].squeeze().numpy() * lonstd + lonmean
lat = X[:, t, :mask[t], 1].squeeze().numpy() * latstd + latmean
fig = plt.figure(figsize=figsize)
# Plot Copenhagen
cph_img = np.asarray(Image.open('images/cph_2.png').convert("L"))
plt.imshow(cph_img, extent=extent, aspect=aspect, cmap='gray')
plt.imshow(LL,
interpolation='bicubic',
cmap='seismic',
aspect='auto',
extent=extent,
vmin=-7,
vmax=2,
alpha=0.7,
origin='lower')
plt.scatter(x=lon, y=lat, color='k', s=30)
# Save figure
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
else:
plt.show()
def process_image(data, folder):
images = np.ndarray([len(data), 32, 96, 1], dtype='int32')
labels = np.ndarray([len(data), 6], dtype='int32')
folder_name = folder
l = len(data)
for i in range(l) :
image = data[i]
image_name = dataset_location + folder_name + '/' + image['filename']
img = Image.open(image_name)
img = img.resize((96,32), PIL.Image.ANTIALIAS)
img = np.asarray(img, dtype="int32")
img = np.dot(img, [[0.2989],[0.5870],[0.1140]])
no_of_digits = len(image['boxes'])
if no_of_digits > 5:
continue
dig = np.array([])
dig = np.append(dig, no_of_digits)
for j in range(no_of_digits) :
digit = image['boxes'][j]['label']
dig = np.append(dig, digit)
zeros = 5 - no_of_digits
for z in range(zeros) :
dig = np.append(dig, 0)
images[i] = img
labels[i] = dig
return(images, labels)
def read_image(filename, flatten=False, dtype=np.float32):
img = Image.open(filename)
img.load()
data = np.asarray(img, dtype=dtype)
if flatten:
data = data.mean(axis=-1)
return data
def test(self):
#template v6
crop_col = [1525, 1588, 1651, 1714, 1777, 1840, 1903, 1966, 2029, 2092, 2155, 2218, 2281, 2344, 2407, 2470, 2532, 2596, 2658, 2722,2798]
crop_row = [892, 936, 981, 1026, 1071, 1115, 1160, 1205, 1250, 1294, 1339, 1384, 1429, 1473, 1518, 1563, 1608, 1653, 1697, 1742, 1787, 1832, 1876, 1921, 1966, 2011, 2055, 2100, 2145, 2190, 2235, 2279, 2324, 2369, 2414, 2458, 2503, 2548, 2593, 2637, 2682, 2727, 2772, 2817, 2861, 2906, 2951, 2996, 3040, 3085, 3130, 3175, 3219, 3264, 3309, 3354, 3399]
label = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: 'X',
}
i = np.random.randint(0,len(self.pathlisttest))
input_img = Image.open(self.pathlisttest[i])
transformed_pil,plot_pil,cropped_data = self.CamScan.cropAll(input_img,crop_col,crop_row,10)
input_shape = self.input_shape
batch_data = np.zeros((len(cropped_data),input_shape[0],input_shape[1],input_shape[2]))
for index in range(len(cropped_data)):
batch_data[index,:,:,:]=np.asarray(cropped_data[index]['image'].resize((self.input_shape[0],self.input_shape[1])))
batch_data = (batch_data/127.5)-1
result = self.model.predict(batch_data)
result = np.argmax(result,axis=1)
result2 = []
for index in range(len(cropped_data)):
result2.append(label[result[index]])
final_img = self.CamScan.plot_result(plot_pil,result2,crop_col,crop_row)
final_img.save(r'images/output_test/'+ os.path.basename(self.pathlisttest[i]))
def parse_img(num_files, DIR, f): current_clothes = np.zeros(shape=(int(num_files),784),dtype=object) for i in range(0,num_files): img = Image.open(DIR+str(f)+'_'+str(i)+'.png') img_arr = np.array(img.getdata(), np.uint8) current_clothes[i,:] = img_arr return current_clothes
def __init__(self, img_path):
response = request.urlopen(url)
image_data = response.read()
self.image = Image.open(BytesIO(image_data))
self.get_exif_data()
super(ImageMetaData, self).__init__()
def transformAllImages(path: str, set_type: str) -> None:
if path is None or not os.path.isdir(path):
raise Exception(f"No path available for source: {path}")
if set_type == "val":
transform = transformValImage
elif set_type == "train":
transform = transformTrainImage
else:
return
def isImg(fn: str) -> bool:
return '.jpg' in fn.casefold()
img_name_list = list(filter(isImg, os.listdir(path)))
for img_name in img_name_list:
img_path = os.path.join(path, img_name)
with Image.open(img_path) as img:
if img.mode == 'L':
img = transforms.Grayscale(num_output_channels=3)(img)
if img.mode == 'CMYK':
img = img.convert('RGB')
try:
img = transform(img)
except Exception as e:
raise Exception(f"{e}\n{img_name}")
img = transforms.ToPILImage()(img)
img.save(img_path)
def pad_and_bg(self,img):
bg_img = np.asarray(Image.open(io.BytesIO( np.random.choice(self.bg_buffer))))
ret_img = img
pad_top = int(abs(np.random.normal(0,self.pad_param)))
pad_bottom = int(abs(np.random.normal(0,self.pad_param)))
pad_left = int(abs(np.random.normal(0,self.pad_param)))
pad_right = int(abs(np.random.normal(0,self.pad_param)))
# trim_top = int(abs(np.random.normal(0,self.trim_param)))
# trim_bottom = int(abs(np.random.normal(0,self.trim_param)))
# trim_left = int(abs(np.random.normal(0,self.trim_param)))
# trim_right = int(abs(np.random.normal(0,self.trim_param)))
ret_img = cv2.copyMakeBorder( np.asarray(ret_img), pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT,value=(255,255,255))
#mask
mask_img = np.zeros((img.size[0],img.size[1],3))
# mask_2 = Image.fromarray(mask_img.astype('uint8')).rotate(rotate_param,resample = Image.NEAREST,expand = True, fillcolor = (255,255,255))
mask_3 = cv2.copyMakeBorder( np.asarray(mask_img), pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT,value=(255,255,255))
ret_img = cv2.resize(ret_img, dsize=(img.size[0], img.size[1]))
bg_img = cv2.resize(bg_img, dsize=(img.size[0], img.size[1]))
mask_3 = cv2.resize(mask_3, dsize=(img.size[0], img.size[1]))
mask_3 = mask_3/255
ret_img = ret_img*(1-mask_3) + bg_img*mask_3
ret_img = Image.fromarray(ret_img.astype('uint8'))
return ret_img
def pad_and_bg(self,img):
bg_img = np.asarray(Image.open(io.BytesIO( np.random.choice(self.bg_buffer))))
ret_img = img
rows,cols = img.size
s_x = int(np.random.normal(0,6))
s_y = int(np.random.normal(0,10))
M = np.float32([[1,0,s_x],[0,1,s_y]])
ret_img = cv2.warpAffine(np.asarray(ret_img),M,(cols,rows),borderMode = cv2.BORDER_CONSTANT,borderValue=(255,255,255))
# ret_img = cv2.copyMakeBorder( np.asarray(ret_img), pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT,value=(255,255,255))
#mask
mask_img = np.zeros((img.size[0],img.size[1],3))
# mask_2 = Image.fromarray(mask_img.astype('uint8')).rotate(rotate_param,resample = Image.NEAREST,expand = True, fillcolor = (255,255,255))
# mask_3 = cv2.copyMakeBorder( np.asarray(mask_img), pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT,value=(255,255,255))
mask_3 = cv2.warpAffine(np.asarray(mask_img),M,(cols,rows),borderMode = cv2.BORDER_CONSTANT,borderValue=(255,255,255))
# ret_img = cv2.resize(ret_img, dsize=(img.size[0], img.size[1]))
bg_img = cv2.resize(bg_img, dsize=(img.size[0], img.size[1]))
# mask_3 = cv2.resize(mask_3, dsize=(img.size[0], img.size[1]))
mask_3 = mask_3/255
ret_img = ret_img*(1-mask_3) + bg_img*mask_3
ret_img = Image.fromarray(ret_img.astype('uint8'))
return ret_img
def rotate_image(filepath):
'''
Rotates images from cellphones if needed by checking exif data, prior to processing.
'''
image = Image.open(filepath)
try:
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == 'Orientation':
break
exif = dict(image._getexif().items())
if exif[orientation] == 3:
print('ROTATING 180')
image = image.rotate(180, expand=True)
elif exif[orientation] == 6:
print('ROTATING 270')
image = image.rotate(270, expand=True)
elif exif[orientation] == 8:
print('ROTATING 90')
image = image.rotate(90, expand=True)
image.save(filepath)
image.close()
except (AttributeError, KeyError, IndexError):
# cases: image don't have getexif
pass
return (image)
def checkValidateCode():
img = Image.open('code.png').convert('L')
ret, img = cv2.threshold(np.array(img), 125, 255, cv2.THRESH_BINARY)
img = Image.fromarray(img.astype(np.uint8))
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract.exe'
code = pytesseract.image_to_string(img)
return code.strip()
def displayLettersAsImage(folders_list):
for folder in folders_list:
letter = folder[-1]
image = random.choice(os.listdir(folder))
path = os.path.join(folder, image)
#can't use iPython display on windows (api problmen)
img = Image.open(path)
img.show()
print(letter)
def preprocess_image(self, img_path):
"""
Used to perform some minor preprocessing on the image before inputting into the network.
"""
im = Image.open(img_path)
im = im.resize((IM_WIDTH, IM_HEIGHT))
im = np.array(im) / 255.0
return im
def test_screen(self):
self.driver.get("http://the-internet.herokuapp.com/")
self.driver.find_element_by_partial_link_text("File Download").click()
self.driver.save_screenshot("screen_file3.png")
time.sleep(3)
size = (0, 0, 680, 400)
im1 = Image.open("screen_file3.png")
im2 = im1.crop(size)
im2.save('element_image.png')
def load_images(file):
f = open(file)
images = []
for line in f:
path = "data/images/" + line.rstrip()
img = Image.open(path)
img = img.resize((224, 224))
images.append(np.array(img))
f.close()
return np.array(images)
def gen_data(self,input_byte,input_path):
img = Image.open(input_byte)
img = img.resize((self.input_shape[0],self.input_shape[1]))
# img = self.pad_and_bg(img)
# rotate_factor = np.random.normal(loc=1.0, scale=10, size=None)
# img = img.rotate(rotate_factor)
# flip_flag = np.random.randint(0,1)
# mirror_flag = np.random.randint(0,1)
# gray_flag = np.random.randint(0,1)
# if(gray_flag):
# img = img.convert('LA')
# if(flip_flag):
# img = ImageOps.flip(img)
# if(mirror_flag):
# img = ImageOps.mirror(img)
blur_rad = np.random.normal(loc=0.0, scale=2, size=None)
img = img.filter(ImageFilter.GaussianBlur(blur_rad))
enhancer_contrat = ImageEnhance.Contrast(img)
enhancer_brightness = ImageEnhance.Brightness(img)
enhancer_color = ImageEnhance.Color(img)
contrast_factor = np.random.normal(loc=1.0, scale=0.5, size=None)
color_factor = np.max([0,1-abs(np.random.normal(loc=0, scale=0.5, size=None))])
rotate_factor = np.random.normal(loc=0, scale=0.17, size=None)
translate_factor_hor = np.random.normal(loc=0, scale=5, size=None)
translate_factor_ver = np.random.normal(loc=0, scale=5, size=None)
brightness_factor = np.random.normal(loc=1.0, scale=0.5, size=None)
img = enhancer_contrat.enhance(contrast_factor)
img = enhancer_brightness.enhance(brightness_factor)
img = enhancer_color.enhance(color_factor)
img = ImageChops.offset(img, int(translate_factor_hor), int(translate_factor_ver))
img = img.rotate(np.rad2deg(rotate_factor))
img.save('test_blur.png')
img = np.asarray(img)/127.5-1
t = os.path.dirname(input_path)[-1:]
# print(t,input_path)
switcher = {
'0': [1,0,0,0,0,0,0],
'1': [0,1,0,0,0,0,0],
'2': [0,0,1,0,0,0,0],
'3': [0,0,0,1,0,0,0],
'4': [0,0,0,0,1,0,0],
'5': [0,0,0,0,0,1,0],
'6': [0,0,0,0,0,0,1],
}
target = switcher.get(t)
# print(t)
return img,target
def set_image_dpi(file_path):
im = Image.open(file_path)
length_x, width_y = im.size
factor = max(1, int(IMAGE_SIZE / length_x))
size = factor * length_x, factor * width_y
# size = (1800, 1800)
im_resized = im.resize(size, Image.ANTIALIAS)
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
temp_filename = temp_file.name
im_resized.save(temp_filename, dpi=(300, 300))
return temp_filename
def load_and_resize_image(image_path, target_size=None) -> Image:
'''
# Returns
# The loaded and resized PIL image
'''
img = Image.open(image_path)
if target_size is None:
return img
else:
return ImageOps.fit(img, target_size, Image.ANTIALIAS)
def __getitem__(self, idx):
image_path = os.path.join(self.images_path, self.images_name[idx])
image = Image.open(image_path)
if len(image.getbands()) == 4: # Fix in png.
im2arr = np.array(image)
image = Image.fromarray(im2arr[:, :, :-1])
image = image.resize((215, 215), Image.BILINEAR)
X = self.transform(image)
return X
def convert_png_to_gif(n_image,
gifdir='figures/frame_dir',
name='multiz',
fps=2):
images = []
for i in range(n_image):
print(i)
a = Image.open(gifdir + '/vol_' + str(i) + '.png')
images.append(a)
imageio.mimsave(gifdir + '/' + name + '.gif', images, fps=fps)
def resize_image(path, size):
for i in dir:
if os.path.isfile(path + item):
image = Image.open(path +
i) #opening any particular image in a file
f, e = os.path.splitext(path + i)
re_image = image.resize(
size, Image.ANTIALIAS) #resizing the image with assigned size.
re_image.save(
f + ' resized.png', 'PNG', quality=90
) #saving the resized image in png format by assigning name.
def augment_by_rotations(folder, prev_cnt):
classes = [os.path.join(folder, d) for d in sorted(os.listdir(folder))] # get list of all sub-folders in folder
for path_to_folder in classes:
if os.path.isdir(path_to_folder):
images = [os.path.join(path_to_folder, i) for i in sorted(os.listdir(path_to_folder)) if i != '.DS_Store']
filename = prev_cnt
for image in images:
im = Image.open(image)
# make 4 copies of each image, with random rotations added in
random_rotate(im, 4, filename, path_to_folder)
filename = filename + 4
print("Finished augmenting " + path_to_folder)
def test_rotations():
img = Image.open("Train/172/bcc000002.bmp")
#img = img.rotate(30)
img = img.resize(dimensions)
rot = make_greyscale_white_bg(img, 127, 127, 127)
rot = invert_colors(rot)
c_color = rot.getpixel((0, 0))
rot = rotate_img(rot, 10, c_color)
w, h = rot.size
rot.show()
return np.array(return_image)
def display(image) :
if type(image) is type([]) :
for images in image :
matplotlib.pyplot.figure()
imshow(images,cmap=matplotlib.pyplot.get_cmap('gray'))
else :
imshow(image,cmap=matplotlib.pyplot.get_cmap('gray'))
# #original image
# In[170]:
image = np.array(Image.open('sample images/sample_image.jpg'))
#imshow(image,cmap=matplotlib.pyplot.get_cmap('grey'))
impr = image_processing()
image = impr.rgb_to_greyscale(image)
display(image)
# #vertical edge detection
# In[171]:
def vertical_edge_detection(self,image) :
kernel = [1,[[-1,0,1],
[-1,0,1],
[-1,0,1]]]
os.path.join(root, d) for d in sorted(os.listdir(root))
if os.path.isdir(os.path.join(root, d))]
if len(data_folders) != num_classes:
raise Exception(
'Expected %d folders, one per class. Found %d instead.' % (
num_classes, len(data_folders)))
print(data_folders)
return data_folders
train_folders = maybe_extract(train_filename)
test_folders = maybe_extract(test_filename)
#%% Problem 1
from PIL import Image
image=Image.open('MDEtMDEtMDAudHRm.png')
image.show()
#%%
image_size = 28 # Pixel width and height.
pixel_depth = 255.0 # Number of levels per pixel.
def load_letter(folder, min_num_images):
"""Load the data for a single letter label."""
image_files = os.listdir(folder)
dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
dtype=np.float32)
image_index = 0
print(folder)
for image in os.listdir(folder):
