quocdat32461997

2 tone
2-step garage
4-beat
4x4 garage
8-bit
acapella
acid
acid breaks
acid house
acid jazz

quocdat32461997

def histogram(img_channel):
        #hist, _ = np.histogram(im_channele, bins = 256, range = (0, 256))
        hist = np.array([0] * 256)

        height = len(img_channel)
        width = len(img_channel[0])
        for row in range(height):
                for col in range(width):
                        hist[img_channel[row, col]] += 1

quocdat32461997

def eq_hist(hist, colors, img_size):
        hist = hist * colors / (2 * img_size)
        cum_hist = hist.cumsum() #Cummulative histogram
        equalized_hist = np.floor(cum_hist)

        return equalized_hist

quocdat32461997

def upcontrastImage(image):
        hist = histogram.histogram(image) 
        stretched_hist = histogram.eq_hist(hist, 256, image.size)

        enhanced_image = np.zeros(shape = image.shape)
        height = len(image)
        width = len(image[0])
        for row in range(height):
                for col in range(width):
                        enhanced_image[row, col] = stretched_hist[image[row, col]]

quocdat32461997

#segmentation.py
"""
	In Computer Vision, segmentation objects and background could be done by different methods:
		Intensity-based Segmentation: Thresholding - Based on the intensity of colors in histogram
		Edge-based Segmentation - Based on edges
		Region-based Segmentation
"""

#import dependencies
from . import histogram

quocdat32461997

#test.py

#import dependencies
import cv2
import os
import MoCV

"""
_segment_image_test - function to test optimal thresholding for image segmentation
Parameters:

quocdat32461997

"""
nn - function nearest neigbor to map pixels of scaled images to the original images for image interpolation. By default, assume that scale around (0, 0)
Parameters:
	orgi_img	I/P	original image input
	scaled_img	I/P	scaled image input - 2d array of mapped pixels
	h_ratio		I/P	scaling ratio for height
	w_ratio		I/P	scaling ratio for width
			O/P	color-filled image
"""
def nn(origi_img, scaled_img, h_ratio, w_ratio):

quocdat32461997

"""
linear_interpolate - function to bilinear interpolatation trasformation
Parameters:
	origi_img	I/P	image input
	scaled_img	I/P	scaled image input - 2d array of mapped pixels
	h_ratio		I/P	scaling ratio for height
	w_ratio		I/P	scaling ratio for width
			O/P	transformed image
"""
def linear_interpolate(origi_img, scaled_img, h_ratio, w_ratio):

quocdat32461997

import tensorflow
from tensorflow.keras.layers import Lambda, Dense, Input
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K

def loss_fn(args):
        return K.constant(1, dtype = 'float32')# creating model
inputs = Input(shape = (784,))
dense1 = Dense(512, activation = 'relu')(inputs)
dense2 = Dense(128, activation = 'relu')(dense1)

quocdat32461997

import tensorflow as tf

def MeanGradientError(outputs, targets, weight):
    filter_x = tf.tile(tf.expand_dims(tf.constant([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype = outputs.dtype), axis = -1), [1, 1, outputs.shape[-1])
    filter_x = tf.tile(tf.expand_dims(filter_x, axis = -1), [1, 1, 1, outputs.shape[-1]])
    filter_y = tf.tile(tf.expand_dims(tf.constant([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype = outputs.dtype), axis = -1), [1, 1, targets.shape[-1]])
    filter_y = tf.tile(tf.expand_dims(filter_y, axis = -1), [1, 1, 1, targets.shape[-1]])

    # output gradient
    output_gradient_x = tf.math.square(tf.nn.conv2d(outputs, filter_x, strides = 1, padding = 'SAME'))