update 133-135(Colorization, ENet, Style-transfer

Author: JimmyHHua

README.md

@@ -170,4 +170,15 @@ code_128 | [DNN- Call the Detection Model of Tensorflow](python/code_128)   | 
 code_129 | [DNN- Call the Openpose Implementation Attitude Assessment](python/code_129)   | ✔️
 code_130 | [DNN- Call YOLO Object Detection Network](python/code_130)   | ✔️
 code_131 | [DNN- YOLOv3-tiny Real-time Object Detection](python/code_131)   | ✔️
-code_132 | [DNN- Single and Multiple Image Detection](python/code_132)   | ✔️
+code_132 | [DNN- Single and Multiple Image Detection](python/code_132)   | ✔️
+code_133 | [DNN- Colorful Image Colorization ](python/code_133)   | ✔️
+code_134 | [DNN- ENet Image Segmentation](python/code_134)   | ✔️
+code_135 | [DNN- Real-time Fast Image Style Transfer](python/code_135)   | ✔️
+
+---
+
+### Appendix
+
+⛳️ The weight can be download from Google Driver：
+
+🌱 [Weight for DNN](https://drive.google.com/drive/folders/1mg6VXpkvEmyL1scaelX5FnW8uw1gk9iq?usp=sharing)

README_CN.md

@@ -170,3 +170,14 @@ code_129 | [DNN- 调用openpose模型实现姿态评估](python/code_129)   | 
 code_130 | [DNN- 调用YOLO对象检测网络](python/code_130)   | ✔️
 code_131 | [DNN- YOLOv3-tiny版本实时对象检测](python/code_131)   | ✔️
 code_132 | [DNN- 单张与多张图像的推断](python/code_132)   | ✔️
+code_133 | [DNN- 图像颜色化模型使用 ](python/code_133)   | ✔️
+code_134 | [DNN- ENet实现图像分割](python/code_134)   | ✔️
+code_135 | [DNN- 实时快速的图像风格迁移](python/code_135)   | ✔️
+
+---
+
+### 附录
+
+⛳️ DNN模块的一些模型下载可以从下面的谷歌云中获取：
+
+🌱 [Weight for DNN](https://drive.google.com/drive/folders/1mg6VXpkvEmyL1scaelX5FnW8uw1gk9iq?usp=sharing)

python/code_133/2.jpg

@@ -0,0 +1,26 @@
+## OpenCV DNN 图像颜色化模型使用 
+
+OpenCV DNN在4.0还支持灰度图像的彩色化模型，是根据2016年ECCV的论文而来，基于卷积神经网络模型，通过对Lab色彩空间进行量化分割，映射到最终的CNN输出结果，最后转换为RGB彩色图像。
+
+相关论文详见：
+
+Arxiv: [https://arxiv.org/pdf/1603.08511.pdf](https://arxiv.org/pdf/1603.08511.pdf)
+
+OpenCV DNN使用该模型时候，除了正常的Caffe模型与配置文件之外，还需要一个Lab的量化表。
+
+原图：
+
+<img src='2.jpg'>
+
+Colorization：
+
+<img src='2color.jpg'>
+
+
+原图：
+
+<img src='4.jpg'>
+
+Colorization：
+
+<img src='4color.jpg'>

python/code_133/2color.jpg

@@ -0,0 +1,53 @@
+import numpy as np
+import cv2 as cv
+
+W_in = 224
+H_in = 224
+modelTxt = "../model/color/colorization_deploy_v2.prototxt";
+modelBin = "../model/color/colorization_release_v2.caffemodel";
+pts_txt = "../model/color/pts_in_hull.npy";
+
+# Select desired model
+net = cv.dnn.readNetFromCaffe(modelTxt, modelBin)
+pts_in_hull = np.load(pts_txt) # load cluster centers
+
+# populate cluster centers as 1x1 convolution kernel
+pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1)
+net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull.astype(np.float32)]
+net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full([1, 313], 2.606, np.float32)]
+
+frame = cv.imread("4.jpg")
+h, w = frame.shape[:2]
+img_rgb = (frame[:,:,[2, 1, 0]] * 1.0 / 255).astype(np.float32)
+
+img_lab = cv.cvtColor(img_rgb, cv.COLOR_RGB2Lab)
+img_l = img_lab[:,:,0] # pull out L channel
+(H_orig,W_orig) = img_rgb.shape[:2] # original image size
+
+# resize image to network input size
+img_rs = cv.resize(img_rgb, (W_in, H_in))
+img_lab_rs = cv.cvtColor(img_rs, cv.COLOR_RGB2Lab)
+img_l_rs = img_lab_rs[:,:,0]
+img_l_rs -= 50 # subtract 50 for mean-centering
+
+# run network
+net.setInput(cv.dnn.blobFromImage(img_l_rs))
+ab_dec = net.forward()[0,:,:,:].transpose((1,2,0))
+
+(H_out,W_out) = ab_dec.shape[:2]
+ab_dec_us = cv.resize(ab_dec, (W_orig, H_orig))
+img_lab_out = np.concatenate((img_l[:,:,np.newaxis],ab_dec_us),axis=2)
+
+img_bgr_out = np.clip(cv.cvtColor(img_lab_out, cv.COLOR_Lab2BGR), 0, 1)
+print(img_bgr_out.shape)
+frame = cv.resize(frame, (w, h))
+cv.imshow('origin', frame)
+gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)
+cv.imshow('gray', gray)
+#cv.imwrite('gray.jpg', gray)
+# fix 4.0 imshow issue
+cv.normalize(img_bgr_out, img_bgr_out, 0, 255, cv.NORM_MINMAX)
+cv.imshow('colorized', cv.resize(np.uint8(img_bgr_out), (w, h)))
+cv.imwrite('4color.jpg', cv.resize(np.uint8(img_bgr_out), (w, h)))
+cv.waitKey(0)
+cv.destroyAllWindows()

python/code_133/4.jpg

@@ -0,0 +1,11 @@
+## OpenCV DNN ENet实现图像分割
+
+OpenCV DNN支持ENet网络模型的图像分割，这里采用的预先训练的ENet网络：
+
+[GitHub - e-lab/ENet-training](https://github.com/e-lab/ENet-training)
+
+基于Cityscapes数据集的训练预测结果
+ 
+<img src=street.jpg>
+
+<img src=enet_result.png>