OpenCV DNN模塊黑白圖片上色修復

大家新年快樂啊，時隔多日，今天又來給博客添磚加瓦了，話不多說，我們先上效果圖吧

圖片左邊部分是原始的黑白影像，右邊是生產的彩色圖片，

這個專案是基于在加利福尼亞大學，伯克利，Richard Zhang，Phillip Isola和Alexei A. Efros開發的研究作業--Colorful Image Colorization

論文地址：https://arxiv.org/pdf/1603.08511.pdf

作者專案github地址：https://github.com/richzhang/colorization/tree/caffe

本人也是在看了之后試了效果的，基于OpenCV DNN模塊給黑白老照片上色(附Python/C++原始碼) (qq.com)https://mp.weixin.qq.com/s/04_UtnP-56MaoOI3d1MSsg

然后，我這添油加醋的寫了一個黑白視頻轉換為彩色視頻的腳本，上代碼

import numpy as np
import matplotlib.pyplot as plt
import cv2

print(cv2.__version__)

# Path of our caffemodel, prototxt, and numpy files
prototxt = "./model/colorization_deploy_v2.prototxt"
caffe_model = "./model/colorization_release_v2.caffemodel"
pts_npy = "./model/pts_in_hull.npy"

# Loading our model
net = cv2.dnn.readNetFromCaffe(prototxt, caffe_model)
pts = np.load(pts_npy)

layer1 = net.getLayerId("class8_ab")
print(layer1)
layer2 = net.getLayerId("conv8_313_rh")
print(layer2)
pts = pts.transpose().reshape(2, 313, 1, 1)
net.getLayer(layer1).blobs = [pts.astype("float32")]
net.getLayer(layer2).blobs = [np.full([1, 313], 2.606, dtype="float32")]

video_path = r'./heibai.mp4'
cap = cv2.VideoCapture(video_path)

fps = 24	#保存視頻的FPS，可以適當調整
fourcc = cv2.VideoWriter_fourcc(*'XVID')
videoWriter = cv2.VideoWriter('video.avi',fourcc,fps,(1296,486))#最后一個是保存圖片的尺寸
i = 0

while cap.isOpened():
    fps = cap.get(cv2.CAP_PROP_FPS)  # 回傳視頻的fps--幀率
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # 回傳視頻的寬
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # 回傳視頻的高
    print('fps:', fps, 'width:', width, 'height:', height)
    ret, ori_frame = cap.read()  # 讀取一幀視頻

    if ret:
        ori_frame = cv2.cvtColor(ori_frame, cv2.COLOR_BGR2GRAY)
        # Convert image from gray scale to RGB format
        ori_frame = cv2.cvtColor(ori_frame, cv2.COLOR_GRAY2RGB)
        normalized = ori_frame.astype("float32") / 255.0
        # Converting the image into LAB
        lab_image = cv2.cvtColor(normalized, cv2.COLOR_RGB2LAB)
        # Resizing the image
        resized = cv2.resize(lab_image, (224, 224))
        # Extracting the value of L for LAB image
        L = cv2.split(resized)[0]
        L -= 50  # OR we can write L = L - 50

        ######inference######
        net.setInput(cv2.dnn.blobFromImage(L))
        # Finding the values of 'a' and 'b'
        ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
        # Resizing
        ab = cv2.resize(ab, (ori_frame.shape[1], ori_frame.shape[0]))

        # Combining L, a, and b channels
        L = cv2.split(lab_image)[0]
        # Combining L,a,b
        LAB_colored = np.concatenate((L[:, :, np.newaxis], ab), axis=2)

        ## Converting LAB image to RGB
        RGB_colored = cv2.cvtColor(LAB_colored, cv2.COLOR_LAB2RGB)
        # Limits the values in array
        RGB_colored = np.clip(RGB_colored, 0, 1)
        # Changing the pixel intensity back to [0,255],as we did scaling during pre-processing and converted the pixel intensity to [0,1]
        RGB_colored = (255 * RGB_colored).astype("uint8")

        RGB_BGR = cv2.cvtColor(RGB_colored, cv2.COLOR_RGB2BGR)
        finall_image = cv2.hconcat((ori_frame,RGB_BGR))
        videoWriter.write(finall_image)
    else:
        break
cap.release()
videoWriter.release()

可以看到的是，運行這個腳本需要先下載3個檔案

為了方便大家運行代碼，所以我給大家準備好了這3個檔案和測驗用的視頻，放在百度云盤了，大家自行下載，

鏈接：https://pan.baidu.com/s/1d7ZDRG8AAWJY8G56tbok2Q
提取碼：gudo

好了，最后在展示一下最后合成的視頻,上鏈接

好了，老規矩，上咩咩圖，

至此，敬禮，salute！！！！！！