python类fastNlMeansDenoising()的实例源码

def apply_filters(self, image, denoise=False):
        """ This method is used to apply required filters to the
            to extracted regions of interest. Every square in a
            sudoku square is considered to be a region of interest,
            since it can potentially contain a value. """
        # Convert to grayscale
        source_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # Denoise the grayscale image if requested in the params
        if denoise:
            denoised_gray = cv2.fastNlMeansDenoising(source_gray, None, 9, 13)
            source_blur = cv2.GaussianBlur(denoised_gray, BLUR_KERNEL_SIZE, 3)
            # source_blur = denoised_gray
        else:
            source_blur = cv2.GaussianBlur(source_gray, (3, 3), 3)
        source_thresh = cv2.adaptiveThreshold(source_blur, 255, 0, 1, 5, 2)
        kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
        source_eroded = cv2.erode(source_thresh, kernel, iterations=1)
        source_dilated = cv2.dilate(source_eroded, kernel, iterations=1)
        if ENABLE_PREVIEW_ALL:
            image_preview(source_dilated)
        return source_dilated

def process_image(img = list()):
    """
    Extracts faces from the image using haar cascade, resizes and applies filters. 
    :param img: image matrix. Must be grayscale
    ::returns faces:: list contatining the cropped face images
    """
    face_cascade = cv2.CascadeClassifier('/Users/mehul/opencv-3.0.0/build/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml')   

    faces_location = face_cascade.detectMultiScale(img, 1.3, 5)
    faces = []

    for (x,y,w,h) in faces_location:
        img = img[y:(y+h), x:(x+w)]
        try:
            img = cv2.resize(img, (256, 256))
        except:
            exit(1)
        img = cv2.bilateralFilter(img,15,10,10)
        img = cv2.fastNlMeansDenoising(img,None,4,7,21)
        faces.append(img)

    return faces

def EdgeDetection(img):
    img = cv2.fastNlMeansDenoising(img,None,3,7,21)
    _,img = cv2.threshold(img,30,255,cv2.THRESH_TOZERO)
    denoise_img = img
    laplacian = cv2.Laplacian(img,cv2.CV_64F)
    sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)  # x
    sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)  # y
    canny = cv2.Canny(img,100,200)
    contour_image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    return {"denoise":denoise_img,"laplacian":laplacian,"canny":canny,"sobely":sobely,"sobelx":sobelx,"contour":contour_image}

# GrayScale Image Convertor
# https://extr3metech.wordpress.com

def EdgeDetection(img):
    img = cv2.fastNlMeansDenoising(img,None,3,7,21)
    _,img = cv2.threshold(img,30,255,cv2.THRESH_TOZERO)
    denoise_img = img
    laplacian = cv2.Laplacian(img,cv2.CV_64F)
    sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)  # x
    sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)  # y
    canny = cv2.Canny(img,100,200)
    contour_image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    return {"denoise":denoise_img,"laplacian":laplacian,"canny":canny,"sobely":sobely,"sobelx":sobelx,"contour":contour_image}

# GrayScale Image Convertor
# https://extr3metech.wordpress.com

def MyDenoiseSobely(path):
    img_gray = ToGrayImage(path)
    img_mydenoise = MyDenoise(img_gray,5)
    img_denoise = cv2.fastNlMeansDenoising(img_mydenoise,None,3,7,21)
    _,img_thre = cv2.threshold(img_denoise,100,255,cv2.THRESH_TOZERO)
    sobely = cv2.Sobel(img_thre,cv2.CV_64F,0,1,ksize=3)
    return sobely

def create_dataset(file_paths, label_set, with_denoising=False):
    data_x = []
    data_y = []

    for path in file_paths:
        single_x = np.asarray(PIL.Image.open(path)).flatten()

        # Denoise image with help of OpenCV (increase time of computing).
        if with_denoising:
            single_x = cv2.fastNlMeansDenoising(single_x).flatten()
        data_x.append(single_x)

    for l in label_set:
        l_to_num = char_to_num(l)
        data_y.append(l_to_num)

    np_data_x = np.array(data_x)
    np_data_y = np.array(data_y)
    return np_data_x, np_data_y


# Use the Keras data generator to augment data.

def MyDenoiseSobely(path):
    img_gray = ToGrayImage(path)
    img_mydenoise = MyDenoise(img_gray,5)
    img_denoise = cv2.fastNlMeansDenoising(img_mydenoise,None,3,7,21)
    _,img_thre = cv2.threshold(img_denoise,100,255,cv2.THRESH_TOZERO)
    sobely = cv2.Sobel(img_thre,cv2.CV_64F,0,1,ksize=3)
    return sobely

def MyDenoiseSobely(path):
    img_gray = ToGrayImage(path)
    img_mydenoise = MyDenoise(img_gray,5)
    img_denoise = cv2.fastNlMeansDenoising(img_mydenoise,None,3,7,21)
    _,img_thre = cv2.threshold(img_denoise,100,255,cv2.THRESH_TOZERO)
    sobely = cv2.Sobel(img_thre,cv2.CV_64F,0,1,ksize=3)
    return sobely

def MyDenoiseSobely(path):
    img_gray = ToGrayImage(path)
    img_mydenoise = MyDenoise(img_gray,5)
    img_denoise = cv2.fastNlMeansDenoising(img_mydenoise,None,3,7,21)
    _,img_thre = cv2.threshold(img_denoise,100,255,cv2.THRESH_TOZERO)
    sobely = cv2.Sobel(img_thre,cv2.CV_64F,0,1,ksize=3)
    return sobely

def MyDenoiseSobely(path):
    img_gray = ToGrayImage(path)
    img_mydenoise = MyDenoise(img_gray,5)
    img_denoise = cv2.fastNlMeansDenoising(img_mydenoise,None,3,7,21)
    _,img_thre = cv2.threshold(img_denoise,100,255,cv2.THRESH_TOZERO)
    sobely = cv2.Sobel(img_thre,cv2.CV_64F,0,1,ksize=3)
    return sobely

def non_local_means_bw_py(imgs, search_window, block_size, photo_render):
    import cv2
    ret_imgs = opencv_wrapper(imgs, cv2.fastNlMeansDenoising, [None,photo_render,block_size,search_window])
    return ret_imgs

def EdgeDetection(img):
    # img = cv2.medianBlur(img,5)
    img = cv2.fastNlMeansDenoising(img,None,3,7,21)
    _,img = cv2.threshold(img,30,255,cv2.THRESH_TOZERO)
    denoise_img = img
    # print(img)
    # cv2.imwrite("Denoise.jpg",img)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()

    # convolute with proper kernels
    laplacian = cv2.Laplacian(img,cv2.CV_64F)
    sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)  # x
    sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)  # y
    # sobel2y = cv2.Sobel(sobely,cv2.CV_64F,0,1,ksize=3)
    # sobelxy = cv2.Sobel(img,cv2.CV_64F,1,1,ksize=5)  # y
    canny = cv2.Canny(img,100,200)
    contour_image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # print(canny)
    # cv2.imwrite('laplacian.jpg',laplacian)
    # cv2.imwrite('sobelx.jpg',sobelx)
    # cv2.imwrite('sobely.jpg',sobely)
    # cv2.imwrite('sobelxy.jpg',sobelxy)
    # cv2.imwrite('canny.jpg',canny)

    # plt.subplot(3,2,1),plt.imshow(img,cmap = 'gray')
    # plt.title('Original'), plt.xticks([]), plt.yticks([])

    # plt.subplot(3,2,2),plt.imshow(laplacian,cmap = 'gray')
    # plt.title('Laplacian'), plt.xticks([]), plt.yticks([])

    # plt.subplot(3,2,3),plt.imshow(sobelx,cmap = 'gray')
    # plt.title('Sobel X'), plt.xticks([]), plt.yticks([])

    # plt.subplot(3,2,4),plt.imshow(sobely,cmap = 'gray')
    # plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])

    # plt.subplot(3,2,4),plt.imshow(sobelxy,cmap = 'gray')
    # plt.title('Sobel XY'), plt.xticks([]), plt.yticks([])

    # plt.subplot(3,2,5),plt.imshow(canny,cmap = 'gray')
    # plt.title('Canny'), plt.xticks([]), plt.yticks([])

    # plt.show()
    # return {"denoise":img}
    return {"denoise":denoise_img,"laplacian":laplacian,"canny":canny,"sobely":sobely,"sobelx":sobelx,"contour":contour_image}