def img_style_transfer(filename, filename2):
img = get_img(filename)
filename_dir = '/Users/tanya/PycharmProjects/image6/static/images/'
content_filename = filename_dir + filename
content_image = load_image(content_filename, max_size=None)
style_filename = filename_dir + filename2
style_image = load_image(style_filename, max_size=300)
content_layer_ids = [4]
style_layer_ids = list(range(13))
res = style_transfer(content_image=content_image,
style_image=style_image,
content_layer_ids=content_layer_ids,
style_layer_ids=style_layer_ids,
weight_content=5,
weight_style=10.0,
weight_denoise=0.3,
num_iterations=60,
step_size=10.0)
# 滤波处理
# 返回处理后文件名称和原始参数
return save_img(res, filename)
def rotate(filename: str, sp: list, size: int, degree: int):
if degree == 90:
angle = Image.ROTATE_90
elif degree == 180:
angle = Image.ROTATE_180
else:
angle = Image.ROTATE_270
# 获取图像
im = get_img(filename)
width, height = im.size
sp[0] = int(sp[0])
sp[1] = int(sp[1])
# 区域起点大于图像的尺寸
if sp[0] > width or sp[1] > height:
return filename
# 设置窗口大小
size_x = width - sp[0] if sp[0] + size > width else size
size_y = height - sp[1] if sp[1] + size > height else size
size = size_x if size_x < size_x else size_y
# 创建矩形窗,左-上-右-下
box = (sp[0], sp[1], sp[0] + size, sp[1] + size)
# 裁剪窗口
region = im.crop(box)
region = region.transpose(angle)
im.paste(region, box)
return save_img(im, filename)
def mosaic(filename: str, sp: list, ep: list, granularity: int) -> tuple:
olddata = '#'.join(sp + ep) + '#' + str(granularity)
# 获取图像
im = get_img(filename)
width, height = im.size
# 创建Draw对象
draw = ImageDraw.ImageDraw(im=im)
sp[0] = int(sp[0])
sp[1] = int(sp[1])
# 区域起点大于图像的尺寸
if sp[0] > width or sp[1] > height or granularity < 1:
return filename, olddata
# 处理终点
ep[0] = width if int(ep[0]) > width else int(ep[0])
ep[1] = height if int(ep[1]) > height else int(ep[1])
# 正式打马赛克
for x in range(int(sp[0]), ep[0], granularity):
for y in range(int(sp[1]), ep[1], granularity):
# 选取小方块中心的像素点填充整个小方块
r, g, b = im.getpixel((x, y))
draw.rectangle([(x, y),
(x + granularity - 1, y + granularity - 1)],
fill=(r, g, b))
# 保存并返回文件名
return save_img(im, filename), olddata
def overlay(filename1, filename2, alpha: float):
im1 = get_img(filename1)
im2 = get_img(filename2)
w = im1.size[0] if im1.size[0] > im2.size[0] else im2.size[0]
h = im1.size[1] if im1.size[1] > im2.size[1] else im2.size[1]
size = (w, h)
base1 = Image.new('RGB', size, ImageColor.getrgb('white'))
base2 = Image.new('RGB', size, ImageColor.getrgb('white'))
base1.paste(im1, (0, 0))
base2.paste(im2, (0, 0))
base1.show()
base2.show()
res = Image.blend(base1, base2, alpha)
res.show()
return save_img(res, filename1)
def water_mask(filename: str, mask_filename: str):
# 获取图像
im = get_img(filename)
width, height = im.size
# 获取水印图片
mask_filename = convert_img(filename, mask_filename)
mask_im = get_img(mask_filename)
mw, mh = mask_im.size
mw, mh = 250, int(mh * 250 / mw)
mask_im = mask_im.resize((mw, mh))
# 裁剪原来的图像区域并混叠
region = im.crop((width - mw, height - mh, width, height))
new = Image.blend(mask_im, region, 0.5)
im.paste(new, (width - mw, height - mh))
return save_img(im, filename)
def img_filter(form, filename, **kwargs):
mode = kwargs.get('mode', None)
img = get_img(filename)
# 读取滤波方式
im_filter, olddata = get_filter(mode, form)
# 滤波处理
res = img.filter(im_filter)
# 返回处理后文件名称和原始参数
return save_img(res, filename), olddata
def img_kernel(filename, size, kernel, scale=None, offset=None):
im = get_img(filename)
if scale is not None and scale.strip() != '':
scale = float(scale)
else:
scale = None
if offset is not None and offset.strip() != '':
offset = float(offset)
else:
offset = 0
im_filter = ImageFilter.Kernel(size, kernel, scale, offset)
res = im.filter(im_filter)
return save_img(res, filename)
def img_enhance(filename, enhance_type, factor):
im = get_img(filename)
if enhance_type == 'Color':
enhancer = ImageEnhance.Color(im)
elif enhance_type == 'Contrast':
enhancer = ImageEnhance.Contrast(im)
elif enhance_type == 'Brightness':
enhancer = ImageEnhance.Brightness(im)
elif enhance_type == 'Sharpness':
enhancer = ImageEnhance.Sharpness(im)
else:
raise NotExistEnhanceError()
res = enhancer.enhance(factor)
return save_img(res, filename)
def text_mask(filename, text, color, font_size, start_point):
olddata = text + '#' + color + '#' + font_size + '#' + start_point[
0] + '#' + start_point[1]
im = get_img(filename)
draw = ImageDraw.ImageDraw(im)
font = ImageFont.truetype(font=basedir +
'/static/font/STHeiti-Light-3.ttc',
size=int(font_size),
encoding='utf-8')
if color in ImageColor.colormap:
color = ImageColor.getrgb(color)
else:
color = ImageColor.getrgb('red')
draw.text((int(start_point[0]), int(start_point[1])),
text,
fill=color,
font=font)
return save_img(im, filename), olddata
net_outp = loaded_model.get_layer(name="softmax").output
test_func = K.function([net_inp], [net_outp])
count = 0
#preprocess data
for index, img in enumerate(list_img):
if K.image_data_format() == 'channels_first':
img_dat = np.expand_dims(img, axis=3)
else:
img_dat = np.expand_dims(img, axis=0)
img_dat = img_dat / 255
c = np.expand_dims(np.array(img_dat).T, axis=0)
top_pred_texts = decode_batch(test_func, c)[0]
plt.subplot(5, 2, index + 1)
plt.imshow(img, cmap="Greys_r")
plt.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' %
(label_list[index], top_pred_texts))
fig = pylab.gcf()
fig.set_size_inches(13, 16)
plt.savefig(
os.path.join(os.path.join(home_dir, "predict"),
"predict%02d.png" % (indx)))
list_img, label_list = get_img(test_data_dir, img_width, img_height)
run_name = "build"
predict(run_name, list_img, label_list, 201)
predict(run_name, list_img, label_list, 301, "gru_model.json",
"gru_weights.h5")
def convert_img(dst, src):
dst_suffix = dst.split('.')[-1]
new_filename = src.split('.')[0] + '.' + dst_suffix
im = get_img(src)
im = im.convert('RGB')
return save_img(im, new_filename)