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package com.reactlibrary; | |
import android.content.Intent; | |
import android.net.Uri; | |
import android.support.v7.app.AppCompatActivity; | |
import android.util.Log; | |
import com.facebook.react.bridge.ReactApplicationContext; | |
import com.github.hiteshsondhi88.libffmpeg.ExecuteBinaryResponseHandler; | |
import com.github.hiteshsondhi88.libffmpeg.FFmpeg; |
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rm -f -r out/Makefile node node_g out/Release/node \ | |
out/Release/node.exp | |
rm -f -r node_modules | |
rm -f test.tap | |
creating ./icu_config.gypi | |
{ 'target_defaults': { 'cflags': [], | |
'default_configuration': 'Release', | |
'defines': [], | |
'include_dirs': [], | |
'libraries': []}, |
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rm -f -r out/Makefile node node_g out/Release/node \ | |
out/Release/node.exp | |
rm -f -r node_modules | |
rm -f test.tap | |
creating ./icu_config.gypi | |
{ 'target_defaults': { 'cflags': [], | |
'default_configuration': 'Release', | |
'defines': [], | |
'include_dirs': [], | |
'libraries': []}, |
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rm -f -r out/Makefile node node_g out/Release/node \ | |
out/Release/node.exp | |
rm -f -r node_modules | |
rm -f test.tap | |
creating ./icu_config.gypi | |
{ 'target_defaults': { 'cflags': [], | |
'default_configuration': 'Release', | |
'defines': [], | |
'include_dirs': [], | |
'libraries': []}, |
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import cv2 | |
from cv2 import COLOR_RGB2GRAY | |
from skimage.feature import hog | |
from sklearn.model_selection import train_test_split | |
from sklearn.svm import LinearSVC | |
import matplotlib.pyplot as plt | |
from glob import glob |
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people_glob = glob('dataset/people/*.png') | |
background_glob = glob('dataset/not-people/*.png') | |
people = [] | |
not_people = [] | |
for filename in people_glob: | |
image = cv2.imread(filename, COLOR_RGB2GRAY) | |
image = cv2.resize(image, (64, 64)) | |
people.append(image) |
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def get_hog_features(image, visualize=False): | |
features = hog( | |
image, | |
orientations=9, | |
pixels_per_cell=(8, 8), | |
cells_per_block=(2, 2), | |
visualize=visualize, | |
feature_vector=True, | |
block_norm='L1' | |
) |
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X = np.vstack([people_features, not_people_features]) | |
y = np.concatenate([np.ones(people_len), np.zeros(not_people_len)]) | |
train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.2, shuffle=True) | |
classifier = LinearSVC(verbose=1) | |
classifier.fit(train_x, train_y) | |
print('Accuracy: %s' % classifier.score(test_x, test_y)) |
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def randcolorvalue(): | |
return float(randint(0, 255)) / 255 | |
def randcolor(): | |
return randcolorvalue(), randcolorvalue(), randcolorvalue() | |
def draw_boxes(image, boxes): | |
image = np.copy(image) |
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test_image = cv2.imread('test.jpg', COLOR_RGB2GRAY) # load test image | |
test_image = cv2.resize(test_image, (200, 400)) # resize | |
# get all sliding windows we want | |
search_windows = \ | |
sliding_window(test_image, y_stop=200, window=(64, 64), overlap=(.7, .7)) + \ | |
sliding_window(test_image, y_stop=250, window=(80, 80), overlap=(.6, .6)) + \ | |
sliding_window(test_image, y_stop=300, window=(96, 96), overlap=(.5, .5)) + \ | |
sliding_window(test_image, y_stop=350, window=(128, 128), overlap=(.4, .4)) |
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