def rotate_img(image, angle, color, filter=Image.NEAREST):
if image.mode == "P" or filter == Image.NEAREST:
matte = Image.new("1", image.size, 1) # mask
else:
matte = Image.new("L", image.size, 255) # true matte
bg = Image.new(image.mode, image.size, color)
bg.paste(image.rotate(angle, filter), matte.rotate(angle, filter))
return bg
def rotate_img(image, angle, color, filter = Image.NEAREST):
if image.mode == "P" or filter == Image.NEAREST:
matte = Image.new("1", image.size, 1) # mask
else:
matte = Image.new("L", image.size, 255) # true matte
bg = Image.new(image.mode, image.size, color)
bg.paste(
image.rotate(angle, filter),
matte.rotate(angle, filter)
)
return bg
def get_concat_v(row1, row2, row3):
dst = Image.new('RGB',
(row1.width, row1.height + row2.height + row3.height))
dst.paste(row1, (0, 0))
dst.paste(row2, (0, row1.height))
dst.paste(row3, (0, row1.height + row2.height))
return dst
def draw_animal_count(response, target_animal='Cow', frame=None):
"""
A function that takes PIL image and draws the count as string from rekognition response.
Returns a full image as PIL
"""
count = 0
for label in response['CustomLabels']:
if label['Name'] == target_animal:
count += 1
image = copy.deepcopy(frame)
number_of_animal = target_animal + ': ' + str(count)
font = ImageFont.truetype('/usr/share/fonts/default/Type1/n019004l.pfb',
50)
x, y = image.size
text_width, text_height = font.getsize(number_of_animal)
color = '#E4E1E1'
button_size = (text_width + 20, text_height + 20)
button_img = Image.new('RGBA', button_size, color)
button_draw = ImageDraw.Draw(button_img)
button_draw.text((10, 10),
str(number_of_animal),
fill='#000000',
font=font)
image.paste(button_img,
((x // 2) - text_width, y - text_height - 20)) #bottom middle
return image
def get_concat_h(im1, im2, im3, im4, im5):
dst = Image.new('RGB', (im1.width + im2.width + im3.width + im4.width +
im5.width, im1.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (im1.width, 0))
dst.paste(im3, (im1.width + im2.width, 0))
dst.paste(im4, (im1.width + im2.width + im3.width, 0))
dst.paste(im5, (im1.width + im2.width + im3.width + im4.width, 0))
return dst
def combine_images_vertical(images, y_offset=0):
widths, heights = zip(*(i.size for i in images))
max_width = max(widths)
total_height = sum(heights)
image = Image.new('RGB', (max_width, total_height + y_offset),
color=(255, 255, 255, 0))
vert_pos = y_offset
for im in images:
width, height = im.size
image.paste(im, (0, vert_pos))
vert_pos += height + y_offset
return image
def highlight(img_pil_deid, coord_df):
# draw = ImageDraw.Draw(img_pil_deid)
# Make a blank image the same size as the image for the rectangle, initialized
# to a fully transparent (0% opaque) version of the tint color, then draw a
# semi-transparent version of the square on it.
overlay = Image.new('RGBA', img_pil_deid.size, TINT_COLOR+(0,))
draw = ImageDraw.Draw(overlay) # Create a context for drawing things on it.
for i,row in coord_df.iterrows():
point = row['coord']
pred_label = iob_to_label(row['ner_label'].lower())
draw.rectangle((row['coord'][:2], row['coord'][2:]), fill=TINT_COLOR+(OPACITY,), outline=TINT_COLOR+(OPACITY,), width=5)
# Alpha composite these two images together to obtain the desired result.
img = Image.alpha_composite(img_pil_deid, overlay)
img = img.convert("RGBA") # Remove alpha for saving in jpg format.
# highlighted_image_list.append(img)
display(img)
def append_images(images, direction='vertical',
bg_color=(255,255,255), aligment='center'):
"""
Appends images
:param images: list: list of images to append
:param direction: direction in which images should be appended
:param bg_color: background color in RGB
:param aligment: aligment of appended images
:return: PIL.Image: resulting Image
"""
widths, heights = zip(*(i.size for i in images))
if direction=='horizontal':
new_width = sum(widths)
new_height = max(heights)
else:
new_width = max(widths)
new_height = sum(heights)
new_im = Image.new('RGB', (new_width, new_height), color=bg_color)
offset = 0
for im in images:
if direction=='horizontal':
y = 0
if aligment == 'center':
y = int((new_height - im.size[1])/2)
elif aligment == 'bottom':
y = new_height - im.size[1]
new_im.paste(im, (offset, y))
offset += im.size[0]
else:
x = 0
if aligment == 'center':
x = int((new_width - im.size[0])/2)
elif aligment == 'right':
x = new_width - im.size[0]
new_im.paste(im, (x, offset))
offset += im.size[1]
return new_im
Data_Sets_Train = []
Data_Sets_Train.append(
[SC_1846_Cortex_Train, Bulk_1846_Cortex, Bulk_1846_Liver])
for dset in Data_Sets_Train:
cell = dset[0]
print cell
os.chdir(os.path.join(basepath, cell))
for file in glob.glob("*sml*.png"):
if "mod" not in file or "blk" not in file:
path = os.path.splitext(file)[0]
basename = os.path.basename(path)
outfile1 = basename + "_blk.png"
if not os.path.isfile(os.path.join(basepath, cell, outfile1)):
#newfile = re.sub("_s\d+__", "-", file)
#shutil.move(file, newfile)
im = Image.open(file)
pixelMap = im.load()
img = Image.new(im.mode, im.size)
pixelsNew = img.load()
for i in range(img.size[0]):
for j in range(img.size[1]):
if pixelMap[i, j] == (250, 250, 250):
pixelsNew[i, j] = (0, 0, 0)
elif pixelMap[i, j] == (255, 255, 255):
pixelsNew[i, j] = (0, 0, 0)
else:
pixelsNew[i, j] = pixelMap[i, j]
img.crop((160, 130, img.size[0], img.size[1])).resize(
(512, 512)).save(outfile1)
print "Done!"
%matplotlib inline
desired_size = 32
im = Image.open(file_name)
old_size = im.size
ratio = float(desired_size)/max(old_size)
new_size = tuple([int(x*ratio) for x in old_size])
new_size
im = im.resize(new_size, Image.ANTIALIAS)
np_im = np.array(im)
np_im.shape
new_im = Image.new("RGB", (desired_size, desired_size))
new_im.paste(im, ((desired_size-new_size[0])//2,
(desired_size-new_size[1])//2))
np_new_im = np.array(new_im)
np_new_im.shape
imshow(np_new_im)
# Simple way to resize image example
size = 32, 32
im.resize(size, Image.ANTIALIAS)
np_im = np.array(im)
np_im.shape
def visualize(bucket, photo, response, display_image=False):
"""
Function that draws bounding boxes based on Rekognition response.
"""
# Load image from S3 bucket
s3_connection = boto3.resource('s3')
s3_object = s3_connection.Object(bucket, photo)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
original_image = Image.open(stream) #just in case we need original image
image = copy.deepcopy(original_image)
imgWidth, imgHeight = image.size
draw = ImageDraw.Draw(image)
for customLabel in response['CustomLabels']:
if 'Geometry' in customLabel:
box = customLabel['Geometry']['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']
fnt = ImageFont.truetype(
'/usr/share/fonts/default/Type1/n019004l.pfb', 20)
text_width, text_height = fnt.getsize(customLabel['Name'])
color = determine_color(customLabel['Name'])
button_size = (text_width + 20, text_height + 20)
button_img = Image.new('RGBA', button_size, color)
button_draw = ImageDraw.Draw(button_img)
button_draw.text((10, 10),
customLabel['Name'],
fill='#000000',
font=fnt)
image.paste(button_img,
(int(left) - 2, int(top - text_height - 20)))
points = ((left, top), (left + width, top), (left + width,
top + height),
(left, top + height), (left, top))
thickness = 5
if customLabel['Name'] == 'cow':
thickness = 7
draw.line(points, fill=color, width=thickness)
dimensions = [imgWidth, imgHeight]
#draw annotations
if display_image == True:
image.show()
img = np.asarray(image).copy()
original_image = np.asarray(original_image).copy()
return img, dimensions, original_image
# In[15]:
#NOMOR SATU
#KONFIGURASI WAKTU
ts = time.time()
#PENYESUAIAN FORMAT WAKTU
waktu = datetime.fromtimestamp(ts).strftime("%d/%m/%Y %H:%M:%S")
#KONFIGURASI ISI TEKS YANG BERISIKAN NIM, NAMA, TANGGAL DAN WAKTU, DAN INOVASI PENAMBAHAN TULISAN
teks = ("18218024 - Yonatan Jori Saputro \n"+ str(waktu) + "\n" + "UAS II3150 18218024" )
#KONFIGURASI GAMBAR TAMBAHAN BERISI TEKS
addedimage = Image.new('RGB', (200, 60), color = (73, 109, 137))
#KONFIGURASI ISI DARI GAMBAR YANG BERISI GAMBAR
fill = ImageDraw.Draw(addedimage)
fill.text((10,10), teks, fill=(255,255,0))
#PENYIMPANAN GAMBAR TAMBAHAN KE DIRECTORY
addedimage.save('addedimage.png')
#LISTING GAMBAR YANG AKAN DIGUNAKAN DALAM GIF
images = ["18218024_Yonatan Jori Saputro.png", "addedimage.png"]
#MENAMBAHKAN FRAME
frames = []
for i in images:
new_frame = Image.open(i)
length = 14
qr = qrcode.QRCode(
version=1,
error_correction=qrcode.constants.ERROR_CORRECT_L,
box_size= length,
border=0,
)
qr.add_data(s1)
qr.make(fit=True)
img = qr.make_image(fill_color="black", back_color="white")
font = ImageFont.truetype("/Library/Fonts/Microsoft/Arial.ttf",30)
background = Image.open("SolarPanelLabel.png")
txt=Image.new('L', (220,50))
d = ImageDraw.Draw(txt)
d.text( (0, 0), "panel_id " + s1, font=font, fill=255)
w=txt.rotate(90, expand=1)
background.paste(img, (720-22*length, 1141-22*length))
draw = ImageDraw.Draw(background)
#draw.text((720-100, 1141-5),"HELOOSLSLLSLS",(0,0,0),font=font)
background.paste(ImageOps.colorize(w, (0,0,0), (0,0,0)), (720-25*length, 1141-280), w)
#background.show()
background = background.save(s1 + ".pdf")
pdfs = [f'{panel_id:05d}' + ".pdf" for panel_id in range(int(startID),int(endID)+1)]
merger = PdfFileMerger()