/
main.py
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main.py
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import os
import cupy as cp
import numpy as np
from matplotlib import pyplot as plt
import cv2
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import RandomFourierFeatures
from sklearn import svm
import sklearn
def read_in_images_new(directory):
vehicle_dir = directory + "/vehicle"
non_dir = directory + "/non"
win_size = 64
win_size_tuple = (win_size, win_size)
cell_size = 8
cell_size_tuple = (cell_size, cell_size)
block_size = (cell_size*2, cell_size*2)
block_stride = (cell_size, cell_size)
nbins = 9
feature_size = int(9 * (4 + ((((win_size/cell_size)-2)*4)*2) + ((((win_size/cell_size)-2) * ((win_size/cell_size)-2))*4)))
hog = cv2.HOGDescriptor(win_size_tuple, block_size, block_stride, cell_size_tuple, nbins)
sum = 0
for filename in os.listdir(vehicle_dir):
sum += 1
for filename in os.listdir(non_dir):
sum += 1
x_array = np.zeros((sum, feature_size))
y_array = np.zeros((sum, 2))
sum = 0
for filename in os.listdir(vehicle_dir):
full_path = vehicle_dir + "/" + filename
img = cv2.imread(full_path, 0)
out = hog.compute(img)
out = np.transpose(out)
out = np.array(out)
x_array[sum] = out
y_array[sum] = np.array([0, 1])
sum += 1
if sum % 100 == 0:
print("Loaded " + str(sum) + " images")
for filename in os.listdir(non_dir):
full_path = non_dir + "/" + filename
img = cv2.imread(full_path, 0)
out = hog.compute(img)
out = np.transpose(out)
out = np.array(out)
x_array[sum] = out
y_array[sum] = np.array([1, 0])
sum += 1
if sum % 100 == 0:
print("Loaded " + str(sum) + " images")
return feature_size, x_array, y_array
def detectEdges(img):
edges = cv2.Canny(img, 100, 200)
plt.imshow(edges, cmap='gray')
plt.show()
def read_in_weights(filename):
file = open(filename, 'r')
string = file.readline()
file_list = string.split(sep=" ")
output_W_size = (int(file_list[0]), int(file_list[1]))
W_output = cp.zeros(output_W_size)
string = file.readline()
file_list = string.split(sep=" ")
for x in range(0, W_output.shape[0]):
for y in range(0, W_output.shape[1]):
W_output[x][y] = float(file_list[x * W_output.shape[1] + y])
string = file.readline()
file_list = string.split(sep=" ")
hidden_W_size = (int(file_list[0]), int(file_list[1]))
W_hidden = cp.zeros(hidden_W_size)
string = file.readline()
file_list = string.split(sep=" ")
for x in range(0, W_hidden.shape[0]):
for y in range(0, W_hidden.shape[1]):
W_hidden[x][y] = float(file_list[x * W_output.shape[1] + y])
string = file.readline()
file_list = string.split(sep=" ")
output_B_size = (int(file_list[0]), int(file_list[1]))
B_output = cp.zeros(output_B_size)
string = file.readline()
file_list = string.split(sep=" ")
for x in range(0, B_output.shape[0]):
for y in range(0, B_output.shape[1]):
B_output[x][y] = float(file_list[x * W_output.shape[1] + y])
string = file.readline()
file_list = string.split(sep=" ")
hidden_B_size = (int(file_list[0]), int(file_list[1]))
B_hidden = cp.zeros(hidden_B_size)
string = file.readline()
file_list = string.split(sep=" ")
for x in range(0, B_hidden.shape[0]):
for y in range(0, B_hidden.shape[1]):
B_hidden[x][y] = float(file_list[x * W_output.shape[1] + y])
file.close()
return W_output, W_hidden, B_output, B_hidden
def sigmoid(input):
return 1 / (1 + cp.exp(-input))
def make_prediction(img, weights):
win_size = 64
win_size_tuple = (win_size, win_size)
cell_size = 8
cell_size_tuple = (cell_size, cell_size)
block_size = (cell_size * 2, cell_size * 2)
block_stride = (cell_size, cell_size)
nbins = 9
feature_size = int(9 * (4 + ((((win_size / cell_size) - 2) * 4) * 2) + (
(((win_size / cell_size) - 2) * ((win_size / cell_size) - 2)) * 4)))
img = cv2.resize(img, win_size_tuple)
hog = cv2.HOGDescriptor(win_size_tuple, block_size, block_stride, cell_size_tuple, nbins)
out = hog.compute(img)
out = cp.transpose(out)
out = cp.array(out)
net_hidden = cp.dot(out, weights[1]) + weights[3]
out_hidden = sigmoid(net_hidden)
net_output = cp.dot(out_hidden, weights[0]) + weights[2]
out_output = sigmoid(net_output)
prediction = cp.argmax(out_output)
return prediction
def make_prediction_new(img, model):
win_size = 64
win_size_tuple = (win_size, win_size)
cell_size = 8
cell_size_tuple = (cell_size, cell_size)
block_size = (cell_size * 2, cell_size * 2)
block_stride = (cell_size, cell_size)
nbins = 9
feature_size = int(9 * (4 + ((((win_size / cell_size) - 2) * 4) * 2) + (
(((win_size / cell_size) - 2) * ((win_size / cell_size) - 2)) * 4)))
img = cv2.resize(img, win_size_tuple)
hog = cv2.HOGDescriptor(win_size_tuple, block_size, block_stride, cell_size_tuple, nbins)
out = hog.compute(img)
out = np.transpose(out)
out = np.array(out)
prediction = model.predict(out)
#prediction = np.argmax(prediction)
return prediction
def detect_car_in_image(img, weights):
window_size = (200, 200)
step = 15
draw_on = img.copy()
draw_on = cv2.cvtColor(draw_on, cv2.COLOR_GRAY2BGR)
window = np.zeros(window_size, dtype=np.uint8)
for x in range(0, img.shape[0]-window_size[0], step):
for y in range(0, img.shape[1]-window_size[1], step):
window = np.zeros(window_size, dtype=np.uint8)
for i in range(0, window_size[0]):
for j in range(0, window_size[1]):
window[i][j] = img[x+i][y+j]
if make_prediction(window, weights) == 1:
cv2.rectangle(draw_on, (y, x), (y+window_size[1], x+window_size[0]), color=(255, 0, 0))
plt.imshow(draw_on)
plt.show()
def detect_car_in_image_new(img, model):
window_size = (100, 100)
step = 25
draw_on = img.copy()
draw_on = cv2.cvtColor(draw_on, cv2.COLOR_GRAY2BGR)
window = np.zeros(window_size, dtype=np.uint8)
for x in range(0, img.shape[0]-window_size[0], step):
for y in range(0, img.shape[1]-window_size[1], step):
window = np.zeros(window_size, dtype=np.uint8)
for i in range(0, window_size[0]):
for j in range(0, window_size[1]):
window[i][j] = img[x+i][y+j]
if make_prediction_new(window, model) == 1:
cv2.rectangle(draw_on, (y, x), (y+window_size[1], x+window_size[0]), color=(255, 0, 0))
plt.imshow(draw_on)
plt.show()
if __name__ == "__main__":
# W_output, W_hidden, B_output, B_hidden = read_in_weights("weights.txt")
# weights = [W_output, W_hidden, B_output, B_hidden]
test_image = cv2.imread("dashcam.jpg", 0)
# detect_car_in_image(test_image, weights)
feature_size, x_train, y_train = read_in_images_new("imgs")
feature_size_test, x_test, y_test = read_in_images_new("test_imgs")
"""inputs = keras.Input(shape=(feature_size,), name="imgs")
x = layers.Dense(32, activation="relu", name="dense_1")(inputs)
outputs = layers.Dense(2, activation="sigmoid", name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)"""
x_train, y_train = sklearn.utils.shuffle(x_train, y_train)
clf = svm.SVC()
y_train = np.argmax(y_train, axis=1)
y_test = np.argmax(y_test, axis=1)
clf.fit(x_train, y_train)
x_val = x_train[-1000:]
y_val = y_train[-1000:]
x_train = x_train[:-1000]
y_train = y_train[:-1000]
"""model.compile(
optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
history = model.fit(
x_train,
y_train,
batch_size=64,
epochs=2,
validation_data=(x_val, y_val),
)
results = model.evaluate(x_test, y_test, batch_size=128)
print("test loss, test acc:", results)"""
detect_car_in_image_new(test_image, clf)