def main():
parser = argparse.ArgumentParser()
parser.add_argument('input', help="input BMP file name")
parser.add_argument("output", help ="output BMP file name")
parser.add_argument("--crop", help="crop to specified are: eg. X1, Y1, X2, Y2")
parser.add_argument('--select-color', help="select one color: red, green or blue")
parser.add_argument('--black-and-white', action='store_true')
parser.add_argument('--rotate', type=int, help="rotate by given number of degree")
parser.add_argument('--blur', action="store_true")
args= parser.parse_args()
data=bmp.read_bmp(args.input)
if args.blur:
data = scipy.ndimage.guassian_filter(data,(3,3,0)) #(0,0,1.5)
if args.select_color:
color = args.select_color.lower([0]) #r,g or b
data=data*np.array(COLOR_VECTORS[color])
if args.crop:
x1, y1, x2, y2 = args.crop.split(',')
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
data = data[y1:y2+1, x1:x2+1]
if args.rotate:
data = scipy.ndimage.rotate(data, args.rotate, prefilter=False)
bmp.write_bmp(args.output, data)
data = bmp.read_bmp(args.input)
bmp.write_bmp(args.output, data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input', help='input BMP file name')
parser.add_argument('output', help='output BMP file name')
parser.add_argument('--crop', help='crop the specifide area: x1,y1,x2,y2')
parser.add_argument('--select_color',
help='select one color: red,green,blue')
parser.add_argument('--rotate')
args = parser.parse_args()
data = bmp.read_bmp(args.input)
if args.rotate:
data = scipy.ndimage.rotate(data, args.rotate, prefilter=False)
if args.select_color:
color = args.select_color.lower()[0] # r, g or b
data = data * np.array(COLOR_VECTORS[color])
if args.crop:
x1, y1, x2, y2 = args.crop.split(',')
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
data = data[y1:y2 + 1, x1:x2 + 1]
bmp.write_bmp(args.output, data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input', help="input BMP file name")
parser.add_argument('output', help="output BMP file name")
parser.add_argument('--crop',
help="crop to specified area, eg. X1,Y1,X2,X2")
parser.add_argument('--select-color',
help="select one color: red, green, blue")
parser.add_argument('--black-and-white', action="store_true")
parser.add_argument('--blur', action="store_true")
parser.add_argument('--rotate', type=int, help="rotate by given degrees")
args = parser.parse_args()
data = bmp.read_bmp(args.input)
if args.blur:
data = scipy.ndimage.gaussian_filter(data, (2, 2, 0))
if args.rotate:
data = scipy.ndimage.rotate(data, args.rotate, prefilter=False)
if args.select_color:
color = args.select_color.lower()[0]
data = data * np.array(COLOR_VECTORS[color])
if args.black_and_white:
data = data.sum(axis=2, keepdims=True) / 3
data = np.ones(3) * data
if args.crop:
x1, y1, x2, y2 = args.crop.split(',')
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
data = data[y1:y2 + 1, x1:x2 + 1]
bmp.write_bmp(args.output, data)
def main():
args = parse_args()
data = bmp.read_bmp(args.input)
if args.rotate:
data = ndimage.rotate(data, angle=args.rotate)
if args.blur:
data = ndimage.gaussian_filter(data, sigma=3)
if args.black_white:
sum_array = data.sum(axis=2, keepdims=True) / 3
data = np.ones(3) * sum_array
if args.color:
data = data * np.array([args.color == 'BLUE', args.color == 'GREEN', args.color == 'RED'])
bmp.write_bmp(args.output, data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input', help='input BMP file name')
parser.add_argument('output', help='output BMP file name')
parser.add_argument('--crop', help='crop to specified area')
parser.add_argument('--select-color',
help='select one color: red, green, blue')
parser.add_argument('--black-and-white', action='store_true')
parser.add_argument('--blur', action='store_true')
parser.add_argument('--rotate',
type=int,
help='rotate by given number of degrees')
args = parser.parse_args()
data = bmp.read_bmp(args.input)
if args.blur:
data = scipy.ndimage.gaussian_filter(data, (6, 1, 1.5))
if args.rotate:
data = scipy.ndimage.rotate(data, args.rotate, prefilter=False)
if args.select_color:
color = args.select_color.lower()[0] # r, g or b
data = data * np.array(COLOR_VECTORS[color])
if args.black_and_white:
data = data.sum(axis=2, keepdims=True) / 3
data = data * np.array([1, 1, 1])
if args.crop:
x1, y1, x2, y2 = args.crop.split(',')
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
data = data[y1:y2 + 1, x1:x2 + 1]
bmp.write_bmp(args.output, data)
pack(SIZE_4, data, 50, dec_bmp['important_colors'])
bmp_palette = bmp.get_palette()
for i in range(len(bmp_palette)):
pack(SIZE_1, data, 54 + i, bmp_palette[i])
offset = dec_bmp['offset']
pack(SIZE_2, data, offset, len(codes))
for i in range(len(codes)):
tmp_offset = offset + 2 + i
pack(U_SIZE_1, data, tmp_offset, codes[i])
f.write(data)
def encode(bmp_data):
codes = []
for i in range(0, IMG_SIZE, BLOCK_SIZE):
for j in range(0, IMG_SIZE, BLOCK_SIZE):
block_tmp = get_block(i, j, bmp_data)
block_encoded = dct.encode(block_tmp) + [-128]
codes += block_encoded
return codes
if __name__ == "__main__":
dec_bmp = bmp.read_bmp('lena.bmp')
bmp_data = dec_bmp['bmp_data']
codes = encode(bmp_data)
pack_code('lena.cbmp', bmp_data, codes)
def cut_out_image(pixel_matrix):
pixels = pixel_matrix[120:354, 140:430]
bmp.write_bmp('tiger_cut_out.bmp', pixels)
def single_color(pixel_matrix, color_index):
mask = np.zeros(3)
mask[color_index] = 1
bmp.write_bmp('single_color.bmp', pixel_matrix * mask)
def rotate_image(pixel_matrix, degrees: float):
bmp.write_bmp(
'rotated_tiger.bmp',
scipy.ndimage.interpolation.rotate(pixel_matrix, degrees, order=1))
def black_and_white(pixel_matrix):
# can use also: scipy.ndimage.filters.gaussian_filter (see sigma param)
sum_array = pixel_matrix.sum(axis=2, keepdims=True) / 3
bmp.write_bmp('black_and_white_tiger.bmp', np.ones(3) * sum_array)
if __name__ == '__main__':
pixels = bmp.read_bmp('tiger.bmp')
cut_out_image(pixels)
single_color(pixels, 1)
rotate_image(pixels, -34.0)
black_and_white(pixels)