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Optimize rendering speed of steering and car video #17

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14 changes: 11 additions & 3 deletions Autopilot/DriveApp.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import numpy as np
import cv2
from keras.models import load_model
import time

model = load_model('models/Autopilot.h5')

Expand All @@ -14,7 +15,7 @@ def keras_predict(model, image):
def keras_process_image(img):
image_x = 40
image_y = 40
img = cv2.resize(img, (image_x, image_y))
img = cv2.resize(img, (image_x, image_y), interpolation=cv2.INTER_LINEAR)
img = np.array(img, dtype=np.float32)
img = np.reshape(img, (-1, image_x, image_y, 1))
return img
Expand All @@ -25,18 +26,25 @@ def keras_process_image(img):
smoothed_angle = 0

cap = cv2.VideoCapture('resources/run.mp4')
cap.set(cv2.CAP_PROP_FPS, 60)
cv2.namedWindow('frame', cv2.WINDOW_NORMAL)
cv2.namedWindow('steering wheel', cv2.WINDOW_NORMAL)
while (cap.isOpened()):
start_time = time.time()
ret, frame = cap.read()
gray = cv2.resize((cv2.cvtColor(frame, cv2.COLOR_RGB2HSV))[:, :, 1], (40, 40))
gray = cv2.resize((cv2.cvtColor(frame, cv2.COLOR_RGB2HSV))[:, :, 1], (40, 40), interpolation=cv2.INTER_LINEAR)
steering_angle = keras_predict(model, gray)
print(steering_angle)
cv2.imshow('frame', cv2.resize(frame, (500, 300), interpolation=cv2.INTER_AREA))
cv2.imshow('frame', cv2.resize(frame, (500, 300), interpolation=cv2.INTER_LINEAR))
smoothed_angle += 0.2 * pow(abs((steering_angle - smoothed_angle)), 2.0 / 3.0) * (
steering_angle - smoothed_angle) / abs(
steering_angle - smoothed_angle)
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), -smoothed_angle, 1)
dst = cv2.warpAffine(steer, M, (cols, rows))
cv2.imshow("steering wheel", dst)
end_time = time.time()
fps = 1 / (end_time - start_time)
print("FPS: ", fps)

if cv2.waitKey(1) & 0xFF == ord('q'):
break
Expand Down
14 changes: 11 additions & 3 deletions Autopilot_V2/AutopilotApp_V2.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import numpy as np
import cv2
from keras.models import load_model
import time

model = load_model('models/Autopilot.h5')

Expand All @@ -14,7 +15,7 @@ def keras_predict(model, image):
def keras_process_image(img):
image_x = 100
image_y = 100
img = cv2.resize(img, (image_x, image_y))
img = cv2.resize(img, (image_x, image_y), interpolation=cv2.INTER_LINEAR)
img = np.array(img, dtype=np.float32)
img = np.reshape(img, (-1, image_x, image_y, 1))
return img
Expand All @@ -25,18 +26,25 @@ def keras_process_image(img):
smoothed_angle = 0

cap = cv2.VideoCapture('run.mp4')
cap.set(cv2.CAP_PROP_FPS, 60)
cv2.namedWindow('frame', cv2.WINDOW_NORMAL)
cv2.namedWindow('steering wheel', cv2.WINDOW_NORMAL)
while (cap.isOpened()):
start_time = time.time()
ret, frame = cap.read()
gray = cv2.resize((cv2.cvtColor(frame, cv2.COLOR_RGB2HSV))[:, :, 1], (100, 100))
gray = cv2.resize((cv2.cvtColor(frame, cv2.COLOR_RGB2HSV))[:, :, 1], (100, 100), interpolation=cv2.INTER_LINEAR)
steering_angle = keras_predict(model, gray)
print(steering_angle)
cv2.imshow('frame', cv2.resize(frame, (600, 400), interpolation=cv2.INTER_AREA))
cv2.imshow('frame', cv2.resize(frame, (600, 400), interpolation=cv2.INTER_LINEAR))
smoothed_angle += 0.2 * pow(abs((steering_angle - smoothed_angle)), 2.0 / 3.0) * (
steering_angle - smoothed_angle) / abs(
steering_angle - smoothed_angle)
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), -smoothed_angle, 1)
dst = cv2.warpAffine(steer, M, (cols, rows))
cv2.imshow("steering wheel", dst)
end_time = time.time()
fps = 1 / (end_time - start_time)
print("FPS: ", fps)

if cv2.waitKey(1) & 0xFF == ord('q'):
break
Expand Down