# USAGE
# python image_drawing_practice.py

# import the necessary packages
import argparse
import re
import cv2
import numpy as np
from skimage.exposure import rescale_intensity

def convolve(image, kernel):
    # grab the spatial dimensions of the image, along with the spatial
    # dimensions of the kernel
    (iH, iW) = image.shape[:2]
    (kH, kW) = kernel.shape[:2]
    
    # allocate memmory for the output image, taking care to "pad" the 
    # borders of the input image so the spatial size (i.e., width and height) are not reduced
    pad = (kW - 1) // 2
    image = cv2.copyMakeBorder(image, pad, pad, pad, pad,
        cv2.BORDER_REPLICATE)
    output = np.zeros((iH, iW), dtype="float32")
    
    # loop over the input image, "sliding" the kernel across each 
    # (x, y)-coordinate from left-to-right and top to bottom
    for y in np.arange(pad, iH + pad):
        for x in np.arange(pad, iW + pad):
            # extract the ROI of the image by extracting the *center* region 
            # of the current (x, y)-coordinates dimensions
            roi = image[y - pad:y + pad + 1, x - pad:x + pad + 1]
            
            # perform the actual convolution by taking the element-wise 
            # mulplicate  between the ROI and the kernel, the summing the 
            # matrix
            k = (roi * kernel).sum()
            
            # store the colvolded value in the output (x, y)-coordinate of 
            # the output image
            output[y - pad, x - pad] = k
    # rescale the output image to be in the range [0, 255]
    output = rescale_intensity(output, in_range=(0, 255))
    output = (output * 255).astype("uint8")
    
    #return the output image
    return output

# construct the argument parser and the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str, required=True,
    help="path to the input image")
args = vars(ap.parse_args())

# construct average blurring kernels used to smooth an image
smallBlur = np.ones((7, 7), dtype="float") * (1.0 / (7 * 7))
largeBlur = np.ones((21, 21), dtype="float") * (1.0 / (21 * 21))

# construct a sharpening filter
sharpen = np.array((
    [0 , -1, 0],
    [-1, 5, -1],
    [0, -1, 0]), dtype="int")

# construct the Laplacian kernel to detect edge-like regions on an image
laplacian = np.array((
    [0, 1, 0],
    [1, -4, 1], 
    [0 ,1 ,0]), dtype="int")

# construct the Sobel x-asis kernel
sobelX = np.array((
    [-1, 0, 1],
    [-2, 0 , 2],
    [-1, 0, 1]), dtype="int")

# construct the Sobel y-axis kernel
sobelY = np.array((
    [-1, -2, -1], 
    [0, 0 ,0],
    [1, 2, 1]), dtype="int")

# construct the kernel bank,  a list of kernels we're going to apply using out 
# custom 'convolve' function and OpenCV's 'filter2D' function
kernelBank = (
    ("small_blur", smallBlur),
    ("large_blur", largeBlur),
    ("sharpen", sharpen),
    ("laplacian", laplacian),
    ("sobel_x", sobelX),
    ("sobel_y", sobelY)
)

# load the input image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# loop over the kernels
for (kernelName, kernel) in kernelBank:
    # apply the kernel to the grayscale image using both our custom 'convolve' 
    # fundtion and OpenCV;s 'filter2D' funstion
    print("[INFO] applying {} kernel".format(kernelName))
    convoleOutput = convolve(gray, kernel)
    opencvOutput = cv2.filter2D(gray, -1, kernel)
    
    # show the output images
    cv2.imshow("original", gray)
    cv2.imshow("{} - convole".format(kernelName), convoleOutput)
    cv2.imshow("{} - opencv".format(kernelName), opencvOutput)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# USAGE
# python image_drawing_practice.py

# import the necessary packages
import argparse
import re
import cv2
import numpy as np
from skimage.exposure import rescale_intensity

def convolve(image, kernel):
    # grab the spatial dimensions of the image, along with the spatial
    # dimensions of the kernel
    (iH, iW) = image.shape[:2]
    (kH, kW) = kernel.shape[:2]
    
    # allocate memmory for the output image, taking care to "pad" the 
    # borders of the input image so the spatial size (i.e., width and height) are not reduced
    pad = (kW - 1) // 2
    image = cv2.copyMakeBorder(image, pad, pad, pad, pad,
        cv2.BORDER_REPLICATE)
    output = np.zeros((iH, iW), dtype="float32")
    
    # loop over the input image, "sliding" the kernel across each 
    # (x, y)-coordinate from left-to-right and top to bottom
    for y in np.arange(pad, iH + pad):
        for x in np.arange(pad, iW + pad):
            # extract the ROI of the image by extracting the *center* region 
            # of the current (x, y)-coordinates dimensions
            roi = image[y - pad:y + pad + 1, x - pad:x + pad + 1]
            
            # perform the actual convolution by taking the element-wise 
            # mulplicate  between the ROI and the kernel, the summing the 
            # matrix
            k = (roi * kernel).sum()
            
            # store the colvolded value in the output (x, y)-coordinate of 
            # the output image
            output[y - pad, x - pad] = k
    # rescale the output image to be in the range [0, 255]
    output = rescale_intensity(output, in_range=(0, 255))
    output = (output * 255).astype("uint8")
    
    #return the output image
    return output

# construct the argument parser and the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str, required=True,
    help="path to the input image")
args = vars(ap.parse_args())

# construct average blurring kernels used to smooth an image
smallBlur = np.ones((7, 7), dtype="float") * (1.0 / (7 * 7))
largeBlur = np.ones((21, 21), dtype="float") * (1.0 / (21 * 21))

# construct a sharpening filter
sharpen = np.array((
    [0 , -1, 0],
    [-1, 5, -1],
    [0, -1, 0]), dtype="int")

# construct the Laplacian kernel to detect edge-like regions on an image
laplacian = np.array((
    [0, 1, 0],
    [1, -4, 1], 
    [0 ,1 ,0]), dtype="int")

# construct the Sobel x-asis kernel
sobelX = np.array((
    [-1, 0, 1],
    [-2, 0 , 2],
    [-1, 0, 1]), dtype="int")

# construct the Sobel y-axis kernel
sobelY = np.array((
    [-1, -2, -1], 
    [0, 0 ,0],
    [1, 2, 1]), dtype="int")

# construct the kernel bank,  a list of kernels we're going to apply using out 
# custom 'convolve' function and OpenCV's 'filter2D' function
kernelBank = (
    ("small_blur", smallBlur),
    ("large_blur", largeBlur),
    ("sharpen", sharpen),
    ("laplacian", laplacian),
    ("sobel_x", sobelX),
    ("sobel_y", sobelY)
)

# load the input image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# loop over the kernels
for (kernelName, kernel) in kernelBank:
    # apply the kernel to the grayscale image using both our custom 'convolve' 
    # fundtion and OpenCV;s 'filter2D' funstion
    print("[INFO] applying {} kernel".format(kernelName))
    convoleOutput = convolve(gray, kernel)
    opencvOutput = cv2.filter2D(gray, -1, kernel)
    
    # show the output images
    cv2.imshow("original", gray)
    cv2.imshow("{} - convole".format(kernelName), convoleOutput)
    cv2.imshow("{} - opencv".format(kernelName), opencvOutput)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# USAGE # python image_drawing_practice.py # import the necessary packages # grab the spatial dimensions of the image, along with the spatial # dimensions of the kernel # allocate memmory for the output image, taking care to "pad" the # borders of the input image so the spatial size (i.e., width and height) are not reduced # loop over the input image, "sliding" the kernel across each # (x, y)-coordinate from left-to-right and top to bottom # extract the ROI of the image by extracting the *center* region # of the current (x, y)-coordinates dimensions # perform the actual convolution by taking the element-wise # mulplicate  between the ROI and the kernel, the summing the # matrix # store the colvolded value in the output (x, y)-coordinate of # the output image # rescale the output image to be in the range [0, 255] #return the output image # construct the argument parser and the arguments # construct average blurring kernels used to smooth an image # construct a sharpening filter # construct the Laplacian kernel to detect edge-like regions on an image # construct the Sobel x-asis kernel # construct the Sobel y-axis kernel # construct the kernel bank,  a list of kernels we're going to apply using out # custom 'convolve' function and OpenCV's 'filter2D' function # load the input image and convert it to grayscale # loop over the kernels # apply the kernel to the grayscale image using both our custom 'convolve' # fundtion and OpenCV;s 'filter2D' funstion # show the output images