Python实现图片美化，醉后不知天在水？（附上代码） | 机器学习_机器学习图片美化

目录

前言

项目说明

项目结构

数据准备

魔改代码

总结

---

前言

根据我的另一篇文章：如何将照片美化，DPED机器学习开源项目安装使用 | 机器学习_阿良的博客-CSDN博客

发现DPED项目需要命令化执行且需要图片目录作为处理源。我还是把项目魔改了一下，简化项目结构，合并到一个python代码中。

模型文件比较大，我提供了下载地址，下载后放到项目对应目录即可。

Github仓库地址：github地址

项目说明

项目结构

看一下项目的结构，如下图：

其中模型文件下载地址：https://pan.baidu.com/s/1IUm8xz5dhh8iW_bLWfihPQ 提取码：TUAN

将imagenet-vgg-verydeep-19.mat下载后放在vgg_pretrained目录中。

环境依赖可以直接参考：Python实现替换照片人物背景，精细到头发丝（附上代码） | 机器学习_阿良的博客-CSDN博客

数据准备

我准备了一张测试图如下：

 

魔改代码

不废话了，上核心代码。

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2021/11/27 13:48
# @Author  : 剑客阿良_ALiang
# @Site    :
# @File    : dped.py
 
# python test_model.py model=iphone_orig dped_dir=dped/ test_subset=full
# iteration=all resolution=orig use_gpu=true
 
import imageio
from PIL import Image
import numpy as np
import tensorflow as tf
import os
import sys
import scipy.stats as st
import uuid
from functools import reduce
 
 
# ---------------------- hy add 2 ----------------------
def log10(x):
    numerator = tf.compat.v1.log(x)
    denominator = tf.compat.v1.log(tf.constant(10, dtype=numerator.dtype))
    return numerator / denominator
 
 
def _tensor_size(tensor):
    from operator import mul
    return reduce(mul, (d.value for d in tensor.get_shape()[1:]), 1)
 
 
def gauss_kernel(kernlen=21, nsig=3, channels=1):
    interval = (2 * nsig + 1.) / (kernlen)
    x = np.linspace(-nsig - interval / 2., nsig + interval / 2., kernlen + 1)
    kern1d = np.diff(st.norm.cdf(x))
    kernel_raw = np.sqrt(np.outer(kern1d, kern1d))
    kernel = kernel_raw / kernel_raw.sum()
    out_filter = np.array(kernel, dtype=np.float32)
    out_filter = out_filter.reshape((kernlen, kernlen, 1, 1))
    out_filter = np.repeat(out_filter, channels, axis=2)
    return out_filter
 
 
def blur(x):
    kernel_var = gauss_kernel(21, 3, 3)
    return tf.nn.depthwise_conv2d(x, kernel_var, [1, 1, 1, 1], padding='SAME')
 
 
def process_command_args(arguments):
    # specifying default parameters
 
    batch_size = 50
    train_size = 30000
    learning_rate = 5e-4
    num_train_iters = 20000
 
    w_content = 10
    w_color = 0.5
    w_texture = 1
    w_tv = 2000
 
    dped_dir = 'dped/'
    vgg_dir = 'vgg_pretrained/imagenet-vgg-verydeep-19.mat'
    eval_step = 1000
 
    phone = ""
 
    for args in arguments:
 
        if args.startswith("model"):
            phone = args.split("=")[1]
 
        if args.startswith("batch_size"):
            batch_size = int(args.split("=")[1])
 
        if args.startswith("train_size"):
            train_size = int(args.split("=")[1])
 
        if args.startswith("learning_rate"):
            learning_rate = float(args.split("=")[1])
 
        if args.startswith("num_train_iters"):
            num_train_iters = int(args.split("=")[1])
 
        # -----------------------------------
 
        if args.startswith("w_content"):
            w_content = float(args.split("=")[1])
 
        if args.startswith("w_color"):
            w_color = float(args.split("=")[1])
 
        if args.startswith("w_texture"):
            w_texture = float(args.split("=")[1])
 
        if args.startswith("w_tv"):
            w_tv = float(args.split("=")[1])
 
        # -----------------------------------
 
        if args.startswith("dped_dir"):
            dped_dir = args.split("=")[1]
 
        if args.startswith("vgg_dir"):
            vgg_dir = args.split("=")[1]
 
        if args.startswith("eval_step"):
            eval_step = int(args.split("=")[1])
 
    if phone == "":
        print("\nPlease specify the camera model by running the script with the following parameter:\n")
        print("python train_model.py model={iphone,blackberry,sony}\n")
        sys.exit()
 
    if phone not in ["iphone", "sony", "blackberry"]:
        print("\nPlease specify the correct camera model:\n")
        print("python train_model.py model={iphone,blackberry,sony}\n")
        sys.exit()
 
    print("\nThe following parameters will be applied for CNN training:\n")
 
    print("Phone model:", phone)
    print("Batch size:", batch_size)
    print("Learning rate:", learning_rate)
    print("Training iterations:", str(num_train_iters))
    print()
    print("Content loss:", w_content)
    print("Color loss:", w_color)
    print("Texture loss:", w_texture)
    print("Total variation loss:", str(w_tv))
    print()
    print("Path to DPED dataset:", dped_dir)
    print("Path to VGG-19 network:", vgg_dir)
    print("Evaluation step:", str(eval_step))
    print()
    return phone, batch_size, train_size, learning_rate, num_train_iters, \
           w_content, w_color, w_texture, w_tv, \
           dped_dir, vgg_dir, eval_step
 
 
def process_test_model_args(arguments):
    phone = ""
    dped_dir = 'dped/'
    test_subset = "small"
    iteration = "all"
    resolution = "orig"
    use_gpu = "true"
 
    for args in arguments:
 
        if args.startswith("model"):
            phone = args.split("=")[1]
 
        if args.startswith("dped_dir"):
            dped_dir = args.split("=")[1]
 
        if args.startswith("test_subset"):
            test_subset = args.split("=")[1]
 
        if args.startswith("iteration"):
            iteration = args.split("=")[1]
 
        if args.startswith("resolution"):
            resolution = args.split("=")[1]
 
        if args.startswith("use_gpu"):
            use_gpu = args.split("=")[1]
 
    if phone == "":
        print("\nPlease specify the model by running the script with the following parameter:\n")
        print(
            "python test_model.py model={iphone,blackberry,sony,iphone_orig,blackberry_orig,sony_orig}\n")
        sys.exit()
 
    return phone, dped_dir, test_subset, iteration, resolution, use_gpu
 
 
def get_resolutions():
    # IMAGE_HEIGHT, IMAGE_WIDTH
 
    res_sizes = {}
 
    res_sizes["iphone"] = [1536, 2048]
    res_sizes["iphone_orig"] = [1536, 2048]
    res_sizes["blackberry"] = [1560, 2080]
    res_sizes["blackberry_orig"] = [1560, 2080]
    res_sizes["sony"] = [1944, 2592]
    res_sizes["sony_orig"] = [1944, 2592]
    res_sizes["high"] = [1260, 1680]
    res_sizes["medium"] = [1024, 1366]
    res_sizes["small"] = [768, 1024]
    res_sizes["tiny"] = [600, 800]
 
    return res_sizes
 
 
def get_specified_res(res_sizes, phone, resolution):
    if resolution == "orig":
        IMAGE_HEIGHT = res_sizes[phone][0]
        IMAGE_WIDTH = res_sizes[phone][1]
    else:
        IMAGE_HEIGHT = res_sizes[resolution][0]
        IMAGE_WIDTH = res_sizes[resolution][1]
 
    IMAGE_SIZE = IMAGE_WIDTH * IMAGE_HEIGHT * 3
 
    return IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE
 
 
def extract_crop(image, resolution, phone, res_sizes):
    if resolution == "orig":
        return image
 
    else:
 
        x_up = int((res_sizes[phone][1] - res_sizes[resolution][1]) / 2)
        y_up = int((res_sizes[phone][0] - res_sizes[resolution][0]) / 2)
 
        x_down = x_up + res_sizes[resolution][1]
        y_down = y_up + res_sizes[resolution][0]
 
        return image[y_up: y_down, x_up: x_down, :]
 
 
# ---------------------- hy add 1 ----------------------
def resnet(input_image):
    with tf.compat.v1.variable_scope("generator"):
        W1 = weight_variable([9, 9, 3, 64], name="W1")
        b1 = bias_variable([64], name="b1")
        c1 = tf.nn.relu(conv2d(input_image, W1) + b1)
 
        # residual 1
 
        W2 = weight_variable([3, 3, 64, 64], name="W2")
        b2 = bias_variable([64], name="b2")
        c2 = tf.nn.relu(_instance_norm(conv2d(c1, W2) + b2))
 
        W3 = weight_variable([3, 3, 64, 64], name="W3")
        b3 = bias_variable([64], name="b3")
        c3 = tf.nn.relu(_instance_norm(conv2d(c2, W3) + b3)) + c1
 
        # residual 2
 
        W4 = weight_variable([3, 3, 64, 64], name="W4")
        b4 = bias_variable([64], name="b4")
        c4 = tf.nn.relu(_instance_norm(conv2d(c3, W4) + b4))
 
        W5 = weight_variable([3, 3, 64, 64], name="W5")
        b5 = bias_variable([64], name="b5")
        c5 = tf.nn.relu(_instance_norm(conv2d(c4, W5) + b5)) + c3
 
        # residual 3
 
        W6 = weight_variable([3, 3, 64, 64], name="W6")
        b6 = bias_variable([64], name="b6")
        c6 = tf.nn.relu(_instance_norm(conv2d(c5, W6) + b6))
 
        W7 = weight_variable([3, 3, 64, 64], name="W7")
        b7 = bias_variable([64], name="b7")
        c7 = tf.nn.relu(_instance_norm(conv2d(c6, W7) + b7)) + c5
 
        # residual 4
 
        W8 = weight_variable([3, 3, 64, 64], name="W8")
        b8 = bias_variable([64], name="b8")
        c8 = tf.nn.relu(_instance_norm(conv2d(c7, W8) + b8))
 
        W9 = weight_variable([3, 3, 64, 64], name="W9")
        b9 = bias_variable([64], name="b9")
        c9 = tf.nn.relu(_instance_norm(conv2d(c8, W9) + b9)) + c7
 
        # Convolutional
 
        W10 = weight_variable([3, 3, 64, 64], name="W10")
        b10 = bias_variable([64], name="b10")
        c10 = tf.nn.relu(conv2d(c9, W10) + b10)
 
        W11 = weight_variable([3, 3, 64, 64], name="W11")
        b11 = bias_variable([64], name="b11")
        c11 = tf.nn.relu(conv2d(c10, W11) + b11)
 
        # Final
 
        W12 = weight_variable([9, 9, 64, 3], name="W12")
        b12 = bias_variable([3], name="b12")
        enhanced = tf.nn.tanh(conv2d(c11, W12) + b12) * 0.58 + 0.5
 
    return enhanced
 
 
def adversarial(image_):
    with tf.compat.v1.variable_scope("discriminator"):
        conv1 = _conv_layer(image_, 48, 11, 4, batch_nn=False)
        conv2 = _conv_layer(conv1, 128, 5, 2)
        conv3 = _conv_layer(conv2, 192, 3, 1)
        conv4 = _conv_layer(conv3, 192, 3, 1)
        conv5 = _conv_layer(conv4, 128, 3, 2)
 
        flat_size = 128 * 7 * 7
        conv5_flat = tf.reshape(conv5, [-1, flat_size])
 
        W_fc = tf.Variable(tf.compat.v1.truncated_normal(
            [flat_size, 1024], stddev=0.01))
        bias_fc = tf.Variable(tf.constant(0.01, shape=[1024]))
 
        fc = leaky_relu(tf.matmul(conv5_flat, W_fc) + bias_fc)
 
        W_out = tf.Variable(
            tf.compat.v1.truncated_normal([1024, 2], stddev=0.01))
        bias_out = tf.Variable(tf.constant(0.01, shape=[2]))
 
        adv_out = tf.nn.softmax(tf.matmul(fc, W_out) + bias_out)
 
    return adv_out
 
 
def weight_variable(shape, name):
    initial = tf.compat.v1.truncated_normal(shape, stddev=0.01)
    return tf.Variable(initial, name=name)
 
 
def bias_variable(shape, name):
    initial = tf.constant(0.01, shape=shape)
    return tf.Variable(initial, name=name)
 
 
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
 
 
def leaky_relu(x, alpha=0.2):
    return tf.maximum(alpha * x, x)
 
 
def _conv_layer(net, num_filters, filter_size, strides, batch_nn=True):
    weights_init = _conv_init_vars(net, num_filters, filter_size)
    strides_shape = [1, strides, strides, 1]
    bias = tf.Variable(tf.constant(0.01, shape=[num_filters]))
 
    net = tf.nn.conv2d(net, weights_init, strides_shape, padding='SAME') + bias
    net = leaky_relu(net)
 
    if batch_nn:
        net = _instance_norm(net)
 
    return net
 
 
def _instance_norm(net):
    batch, rows, cols, channels = [i.value for i in net.get_shape()]
    var_shape = [channels]
 
    mu, sigma_sq = tf.compat.v1.nn.moments(net, [1, 2], keepdims=True)
    shift = tf.Variable(tf.zeros(var_shape))
    scale = tf.Variable(tf.ones(var_shape))
 
    epsilon = 1e-3
    normalized = (net - mu) / (sigma_sq + epsilon) ** (.5)
 
    return scale * normalized + shift
 
 
def _conv_init_vars(net, out_channels, filter_size, transpose=False):
    _, rows, cols, in_channels = [i.value for i in net.get_shape()]
 
    if not transpose:
        weights_shape = [filter_size, filter_size, in_channels, out_channels]
    else:
        weights_shape = [filter_size, filter_size, out_channels, in_channels]
 
    weights_init = tf.Variable(
        tf.compat.v1.truncated_normal(
            weights_shape,
            stddev=0.01,
            seed=1),
        dtype=tf.float32)
    return weights_init
 
 
# ---------------------- hy add 0 ----------------------
 
def beautify(pic_path: str, output_dir: str, gpu='1'):
    tf.compat.v1.disable_v2_behavior()
 
    # process command arguments
    phone = "iphone_orig"
    test_subset = "full"
    iteration = "all"
    resolution = "orig"
 
    # get all available image resolutions
    res_sizes = get_resolutions()
 
    # get the specified image resolution
    IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = get_specified_res(
        res_sizes, phone, resolution)
 
    if gpu == '1':
        use_gpu = 'true'
    else:
        use_gpu = 'false'
 
    # disable gpu if specified
    config = tf.compat.v1.ConfigProto(
        device_count={'GPU': 0}) if use_gpu == "false" else None
 
    # create placeholders for input images
    x_ = tf.compat.v1.placeholder(tf.float32, [None, IMAGE_SIZE])
    x_image = tf.reshape(x_, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
 
    # generate enhanced image
    enhanced = resnet(x_image)
 
    with tf.compat.v1.Session(config=config) as sess:
        # test_dir = dped_dir + phone.replace("_orig",
        #                                     "") + "/test_data/full_size_test_images/"
        # test_photos = [f for f in os.listdir(
        #     test_dir) if os.path.isfile(test_dir + f)]
        test_photos = [pic_path]
 
        if test_subset == "small":
            # use five first images only
            test_photos = test_photos[0:5]
 
        if phone.endswith("_orig"):
 
            # load pre-trained model
            saver = tf.compat.v1.train.Saver()
            saver.restore(sess, "models_orig/" + phone)
 
            for photo in test_photos:
                # load training image and crop it if necessary
                new_pic_name = uuid.uuid4()
                print(
                    "Testing original " +
                    phone.replace(
                        "_orig",
                        "") +
                    " model, processing image " +
                    photo)
                image = np.float16(np.array(
                    Image.fromarray(imageio.imread(photo)).resize([res_sizes[phone][1], res_sizes[phone][0]]))) / 255
 
                image_crop = extract_crop(
                    image, resolution, phone, res_sizes)
                image_crop_2d = np.reshape(image_crop, [1, IMAGE_SIZE])
 
                # get enhanced image
 
                enhanced_2d = sess.run(enhanced, feed_dict={x_: image_crop_2d})
                enhanced_image = np.reshape(
                    enhanced_2d, [IMAGE_HEIGHT, IMAGE_WIDTH, 3])
 
                before_after = np.hstack((image_crop, enhanced_image))
                photo_name = photo.rsplit(".", 1)[0]
 
                # save the results as .png images
 
                # imageio.imwrite(
                #     "visual_results/" +
                #     phone +
                #     "_" +
                #     photo_name +
                #     "_enhanced.png",
                #     enhanced_image)
                imageio.imwrite(os.path.join(output_dir, '{}.png'.format(new_pic_name)), enhanced_image)
                # imageio.imwrite(
                #     "visual_results/" +
                #     phone +
                #     "_" +
                #     photo_name +
                #     "_before_after.png",
                #     before_after)
                imageio.imwrite(os.path.join(output_dir, '{}_before_after.png'.format(new_pic_name)), before_after)
                return os.path.join(output_dir, '{}.png'.format(new_pic_name))
        else:
            num_saved_models = int(len([f for f in os.listdir(
                "models_orig/") if f.startswith(phone + "_iteration")]) / 2)
 
            if iteration == "all":
                iteration = np.arange(1, num_saved_models) * 1000
            else:
                iteration = [int(iteration)]
 
            for i in iteration:
 
                # load pre-trained model
                saver = tf.compat.v1.train.Saver()
                saver.restore(
                    sess,
                    "models_orig/" +
                    phone +
                    "_iteration_" +
                    str(i) +
                    ".ckpt")
 
                for photo in test_photos:
                    # load training image and crop it if necessary
                    new_pic_name = uuid.uuid4()
                    print("iteration " + str(i) + ", processing image " + photo)
                    image = np.float16(np.array(
                        Image.fromarray(imageio.imread(photo)).resize(
                            [res_sizes[phone][1], res_sizes[phone][0]]))) / 255
 
                    image_crop = extract_crop(
                        image, resolution, phone, res_sizes)
                    image_crop_2d = np.reshape(image_crop, [1, IMAGE_SIZE])
 
                    # get enhanced image
 
                    enhanced_2d = sess.run(enhanced, feed_dict={x_: image_crop_2d})
                    enhanced_image = np.reshape(
                        enhanced_2d, [IMAGE_HEIGHT, IMAGE_WIDTH, 3])
 
                    before_after = np.hstack((image_crop, enhanced_image))
                    photo_name = photo.rsplit(".", 1)[0]
 
                    # save the results as .png images
                    # imageio.imwrite(
                    #     "visual_results/" +
                    #     phone +
                    #     "_" +
                    #     photo_name +
                    #     "_iteration_" +
                    #     str(i) +
                    #     "_enhanced.png",
                    #     enhanced_image)
                    imageio.imwrite(os.path.join(output_dir, '{}.png'.format(new_pic_name)), enhanced_image)
                    # imageio.imwrite(
                    #     "visual_results/" +
                    #     phone +
                    #     "_" +
                    #     photo_name +
                    #     "_iteration_" +
                    #     str(i) +
                    #     "_before_after.png",
                    #     before_after)
                    imageio.imwrite(os.path.join(output_dir, '{}_before_after.png'.format(new_pic_name)), before_after)
                    return os.path.join(output_dir, '{}.png'.format(new_pic_name))
 
 
if __name__ == '__main__':
    print(beautify('C:/Users/yi/Desktop/6.jpg', 'result/'))

 

代码说明

1、beautify方法有3个参数，分别为图片地址、输出目录、是否使用gpu（默认使用）。

2、输出的图片有两张，为了文件名不重复，使用uuid作为文件名，后缀带_before_after为对比图。

3、没有对文件做校验，如果需要可以自行添加。

验证一下效果

怎么样？很炫吧。

总结

研究这个项目还是很有意思的，记录与分享又是另一种快乐。以前以为自己是个开朗的人，现在细细翻阅自己的人生，更多的是宁静与孤独。以前曾看书看马尔克斯的一句话：在变老的路上，与其抗拒孤独，不如学会享受孤独。所以孤独没什么不好的，沉浸在程序的另一个世界里，就是另一个心情。

分享：

        生命从来不曾离开过孤独而独立存在。无论是我们出生、我们成长、我们相爱还是我们成功失败，直到最后的最后，孤独犹如影子一样存在于生命一隅。——《马尔克斯》

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