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Commit 6b990991 authored by kpp55's avatar kpp55
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Merge branch 'master' of... 

Merge branch 'master' of https://gitlab.cci.drexel.edu/kpp55/Autonomous_Vehicles_with_embedded_intelligence
parents 32fbfd9c c9a27b75
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Detect/detect.py
+ 64
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View file @ 6b990991
#/!usr/bin/python #!/usr/bin/python
import numpy as np import numpy as np
#import rospy #import rospy
...@@ -35,7 +35,7 @@ def detect_edge(pic): ...@@ -35,7 +35,7 @@ def detect_edge(pic):
# pic: original image to apply the pre-set region of interest too # pic: original image to apply the pre-set region of interest too
def ROI(pic): def ROI(pic):
height = pic.shape[0] height = pic.shape[0]
triangle = np.array([[(100, height), (600, height), (350, 100)]]) triangle = np.array([[(250, height), (1100, height), (550, 250)]])
mask = np.zeros_like(pic) mask = np.zeros_like(pic)
cv2.fillPoly(mask, triangle, 255) cv2.fillPoly(mask, triangle, 255)
roi = cv2.bitwise_and(pic, mask) roi = cv2.bitwise_and(pic, mask)
...@@ -46,7 +46,7 @@ def ROI(pic): ...@@ -46,7 +46,7 @@ def ROI(pic):
# pic: original image to apply the pre-set region of interest too # pic: original image to apply the pre-set region of interest too
def ROI_real(pic): def ROI_real(pic):
height = pic.shape[0] height = pic.shape[0]
triangle = np.array([[(0, height), (620, height), (430, 250), (150, 250)]]) triangle = np.array([[(0, height), (145, 300), (475, 300), (600, height)]], dtype=np.int32)
mask = np.zeros_like(pic) mask = np.zeros_like(pic)
cv2.fillPoly(mask, triangle, 255) cv2.fillPoly(mask, triangle, 255)
roi = cv2.bitwise_and(pic, mask) roi = cv2.bitwise_and(pic, mask)
...@@ -56,7 +56,7 @@ def ROI_real(pic): ...@@ -56,7 +56,7 @@ def ROI_real(pic):
# params # params
# edge_pic: the image with the edge detection performed # edge_pic: the image with the edge detection performed
def getLines(edge_pic): def getLines(edge_pic):
return cv2.HoughLinesP(edge_pic, 1, np.pi / 180, 100, maxLineGap=80, minLineLength=20) return cv2.HoughLinesP(edge_pic, 1, np.pi / 180, 50, maxLineGap=80, minLineLength=10)
# Apply the passed in lines the the original picture # Apply the passed in lines the the original picture
# params # params
...@@ -82,6 +82,35 @@ def applyLines(original_pic, lines): ...@@ -82,6 +82,35 @@ def applyLines(original_pic, lines):
return original_pic return original_pic
def find_middle(leftPoints, rightPoints):
middle_lines = [[],[]]
if(leftPoints[1] is None or rightPoints[1] is None
):
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Caught the empty list~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
return 0
else:
print(leftPoints[1])
print(rightPoints[1])
for x in range(150):
#print("Right x point: " + str(rightPoints[0][x]))
#print("Left x point: " + str(leftPoints[0][x]))
#print("Right Y point: " + str(rightPoints[1][x]))
#print("Left Y point: " + str(leftPoints[1][x]))
midPoint = (rightPoints[0][149-x] + leftPoints[0][x]) / 2
#print("midPoint X: " + str(midPoint))
#print("midPoint Y: " + str(leftPoints[1][x]))
middle_lines[1].append(leftPoints[1][x])
middle_lines[0].append(midPoint)
return middle_lines
# Find the two average lines given the set of lines # Find the two average lines given the set of lines
# params # params
# pic: the original image # pic: the original image
...@@ -168,13 +197,13 @@ def find_average_lane(pic, lines): ...@@ -168,13 +197,13 @@ def find_average_lane(pic, lines):
for line in lines: for line in lines:
x1, y1, x2, y2 = line[0] x1, y1, x2, y2 = line[0]
parameters = np.polyfit((x1, x2), (y1, y2), 1) parameters = np.polyfit((x1, x2), (y1, y2), 1)
print("Slope, intercept") #print("Slope, intercept")
print(parameters) #print(parameters)
slope = parameters[0] slope = parameters[0]
intercept = parameters[1] intercept = parameters[1]
if(slope < 0): if(slope < 0):
print("Left insert") #print("Left insert")
left_lines.append((slope, intercept)) left_lines.append((slope, intercept))
left_lines_points[0].append(x1) left_lines_points[0].append(x1)
left_lines_points[0].append(x2) left_lines_points[0].append(x2)
...@@ -182,7 +211,7 @@ def find_average_lane(pic, lines): ...@@ -182,7 +211,7 @@ def find_average_lane(pic, lines):
left_lines_points[1].append(y2) left_lines_points[1].append(y2)
else: else:
print("Right insert") #print("Right insert")
right_lines.append((slope, intercept)) right_lines.append((slope, intercept))
right_lines_points[0].append(x1) right_lines_points[0].append(x1)
right_lines_points[0].append(x2) right_lines_points[0].append(x2)
...@@ -190,34 +219,34 @@ def find_average_lane(pic, lines): ...@@ -190,34 +219,34 @@ def find_average_lane(pic, lines):
right_lines_points[1].append(y2) right_lines_points[1].append(y2)
if not left_lines: if not left_lines:
print("Left is empty") #print("Left is empty")
left_line = [0, 0, 0, 0] left_line = [0, 0, 0, 0]
else: else:
left_average = np.average(left_lines, axis=0) left_average = np.average(left_lines, axis=0)
left_line = make_coordinates(pic, left_average) left_line = make_coordinates(pic, left_average)
print("Left Line: ") #print("Left Line: ")
print(left_line) #print(left_line)
if not right_lines: if not right_lines:
print("Right is emtpy") #print("Right is emtpy")
right_line = [0, 0, 0, 0] right_line = [0, 0, 0, 0]
else: else:
right_average = np.average(right_lines, axis=0) right_average = np.average(right_lines, axis=0)
right_line = make_coordinates(pic, right_average) right_line = make_coordinates(pic, right_average)
print("Right line : ") #print("Right line : ")
print(right_line) #print(right_line)
print("Left fit") #print("Left fit")
print(left_line) #print(left_line)
print("\nRight fit") #print("\nRight fit")
print(right_line) #print(right_line)
return np.array([left_line, right_line]) return np.array([left_line, right_line])
def make_coordinates(image, line_parameters): def make_coordinates(image, line_parameters):
print(line_parameters) #print(line_parameters)
slope, intercept = line_parameters slope, intercept = line_parameters
y1 = image.shape[0] y1 = image.shape[0]
y2 = int(y1*(1/2)) y2 = int(y1*(1/2))
...@@ -257,27 +286,29 @@ def detectDeparture(left, car, right): ...@@ -257,27 +286,29 @@ def detectDeparture(left, car, right):
#video = cv2.VideoCapture("test2.mp4") video = cv2.VideoCapture("test2.mp4")
#video = cv2.VideoCapture("highway.mp4") #video = cv2.VideoCapture("highway.mp4")
video = cv2.VideoCapture(1) #video = cv2.VideoCapture(1)
plt.ion() plt.ion()
while True: while True:
ret, frame = video.read() ret, frame = video.read()
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# frame = cv2.imread("../frame.jpeg")
frame_edge = detect_edge(frame) frame_edge = detect_edge(frame)
if not ret: if not ret:
video = cv2.VideoCapture(0) video = cv2.VideoCapture(1)
continue continue
new_img = formatImg(frame) new_img = formatImg(frame)
wEdges = detect_edge(new_img) wEdges = detect_edge(new_img)
cropped = ROI_real(wEdges) #cropped = ROI_real(wEdges)
cropped = ROI(wEdges)
lines = getLines(cropped) lines = getLines(cropped)
...@@ -287,14 +318,18 @@ while True: ...@@ -287,14 +318,18 @@ while True:
else: else:
Rpoints, Lpoints = find_poly_lane(new_img, lines) Rpoints, Lpoints = find_poly_lane(new_img, lines)
Mpoints = find_middle(Lpoints, Rpoints)
#(type(Rpoints[0][0])) #(type(Rpoints[0][0]))
plt.cla() plt.cla()
plt.clf() plt.clf()
plt.scatter(Rpoints[0], Rpoints[1]) plt.scatter(Rpoints[0], Rpoints[1])
plt.scatter(Lpoints[0], Lpoints[1]) plt.scatter(Lpoints[0], Lpoints[1])
plt.scatter(Mpoints[0], Mpoints[1])
plt.scatter(310, 300) #plt.scatter(310, 300)
plt.imshow(frame, zorder=0) plt.imshow(frame, zorder=0)
...@@ -305,9 +340,9 @@ while True: ...@@ -305,9 +340,9 @@ while True:
lane = applyLines(frame, lines) lane = applyLines(frame, lines)
cv2.imshow("edges", wEdges) #cv2.imshow("edges", wEdges)
cv2.imshow("cropped", cropped) #cv2.imshow("cropped", cropped)
cv2.imshow("frame", lane) #cv2.imshow("frame", lane)
key = cv2.waitKey(25) key = cv2.waitKey(25)
if key == 27: if key == 27:
...@@ -315,27 +350,3 @@ while True: ...@@ -315,27 +350,3 @@ while True:
video.release() video.release()
cv2.destroyAllWindows() cv2.destroyAllWindows()
'''
# Load image
img = cv2.imread('lol_image.jpg')
new_img = formatImg(img)
wEdges = detect_edge(new_img)
cropped = ROI(wEdges)
lines = getLines(cropped)
#lanes = applyLines(img, lines)
average_lines = find_average_lane(img, lines)
average_lanes = applyLines(img, average_lines)
cv2.imshow("edges", wEdges)
#cv2.imshow("with lines", lanes)
cv2.imshow("Average", average_lanes)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
\ No newline at end of file
Neural_Network_Development/resnet/resnet_predict.py 0 → 100644
+ 40
− 0
View file @ 6b990991
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
import os
from keras.applications.resnet50 import preprocess_input, decode_predictions
from keras.applications.resnet50 import ResNet50
# image folder
#folder_path = 'data/test/'
img_path = 'data/test/stop_1405371717.avi_image7.png'
# path to model
#model_path = 'resnet_stop.h5'
model = ResNet50(weights='imagenet')
model.summary()
# dimensions of images
img_width, img_height = 224, 224
# load the trained model
#model = load_model(model_path)
model = load_model(model)
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])
"""
#images = []
for img in os.listdir(folder_path):
img = image.load_img(folder_path + img, target_size=(img_width, img_height))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)
preds = model.predict(img)
print('Predicted:', decode_predictions(preds, top=3)[0])
"""
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])
Neural_Network_Development/resnet/resnet_train.py 0 → 100644
+ 84
− 0
View file @ 6b990991
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
img_width, img_height = 224, 224
train_data_dir = "data/train"
validation_data_dir = "data/valid"
nb_train_samples = 745
nb_validation_samples = 181
batch_size = 16
epochs = 5
model = applications.ResNet50(include_top=False, weights='imagenet', input_shape=(img_width, img_height, 3),classes=1001)
model.summary()
# Freeze the layers which you don't want to train.
for layer in model.layers[:]:
layer.trainable = False
# Adding custom Layer
# We only add
x = model.output
x = Flatten()(x)
# Adding even more custom layers
# x = Dense(1024, activation="relu")(x)
# x = Dropout(0.5)(x)
# x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation="softmax")(x)
# creating the final model
model_final = Model(model.input, predictions)
# compile the model
model_final.compile(loss = "sparse_categorical_crossentropy", optimizer="sgd", metrics=["accuracy"])
#optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=["accuracy"])
# Initiate the train and test generators with data Augumentation
train_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.1,
width_shift_range = 0.1,
height_shift_range=0.1,
rotation_range=10)
test_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.1,
width_shift_range = 0.1,
height_shift_range=0.1,
rotation_range=10)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size = (img_height, img_width),
batch_size = batch_size,
class_mode = "categorical",shuffle=True)
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size = (img_height, img_width),
class_mode = "categorical", shuffle=True)
# Save the model according to the conditions
checkpoint = ModelCheckpoint("resnet_stop.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
# Train the model
model_final.fit_generator(
train_generator,
steps_per_epoch = nb_train_samples/batch_size,
epochs = epochs,
validation_data = validation_generator,
validation_steps = nb_validation_samples/batch_size,
callbacks = [checkpoint, early])
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