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# datishere quocdat32461997

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Focusing
Last active Nov 27, 2020
 import tensorflow as tf def MeanGradientError(outputs, targets, weight): filter_x = tf.tile(tf.expand_dims(tf.constant([[-1, -2, -2], [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'))
Created Apr 7, 2020
View demo.py
 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)
Created Feb 8, 2020
View bilinear_interpolate.py
 """ 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):
Created Feb 8, 2020
View nn.py
 """ 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):
Created Feb 1, 2020
View segmentation_test.py
 #test.py #import dependencies import cv2 import os import MoCV """ _segment_image_test - function to test optimal thresholding for image segmentation Parameters:
Last active Feb 1, 2020
View segmentation.py
 #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
Last active Jan 27, 2020
View contrastImage.py
 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) for row in range(height): for col in range(width): enhanced_image[row, col] = stretched_hist[image[row, col]]
Last active Jan 27, 2020
View eq_hist.py
 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
Last active Jan 27, 2020
Compute image histogram
View histogram.py
 def histogram(img_channel): #hist, _ = np.histogram(im_channele, bins = 256, range = (0, 256)) hist = np.array( * 256) height = len(img_channel) width = len(img_channel) for row in range(height): for col in range(width): hist[img_channel[row, col]] += 1
Created Nov 11, 2019 — forked from sampsyo/genres.txt
music genre list scraper
View genres.txt
 2 tone 2-step garage 4-beat 4x4 garage 8-bit acapella acid acid breaks acid house acid jazz