def run(self):
f = next(self.stream)
frame = f.array
self.rawCapture.truncate(0)
if self.image_d == 1:
frame = rgb2gray(frame)
return frame
def update(self):
# keep looping infinitely until the thread is stopped
for f in self.stream:
# grab the frame from the stream and clear the stream in
# preparation for the next frame
self.frame = f.array
self.rawCapture.truncate(0)
if self.image_d == 1:
self.frame = rgb2gray(self.frame)
# if the thread indicator variable is set, stop the thread
if not self.on:
break
def update(self):
from datetime import datetime, timedelta
import pygame.image
while self.on:
start = datetime.now()
if self.cam.query_image():
# snapshot = self.cam.get_image()
# self.frame = list(pygame.image.tostring(snapshot, "RGB", False))
snapshot = self.cam.get_image()
snapshot1 = pygame.transform.scale(snapshot, self.resolution)
#************************
snapshot2 = pygame.transform.rotate(pygame.transform.flip(snapshot1, True, False), -90)
frame1 = pygame.surfarray.pixels3d(snapshot2)
greenLower = (30, 45, 70) # Set boundary for green (HSV Color Space)
greenUpper = (64, 240, 240)
img = frame1.copy() # Import image
blurred = cv2.GaussianBlur(img, (11, 11), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_RGB2HSV) # Transfer to HSV color space
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2) # Create mask to find green areas
mask = cv2.dilate(mask, None, iterations=2)
# cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# if cnts[1] == []:
# x, y, radius = [0, 0, 0]
# else:
# c = max(cnts[1], key=cv2.contourArea)
# ((y, x), radius) = cv2.minEnclosingCircle(c) # get center and radius of the ball
# # img = cv2.circle(img, (int(y), int(x)), int(radius), (255, 0, 0), 2)
# for row in range(mask.shape[0]):
# for col in range(mask.shape[1]):
# if (row-x)**2 + (col-y)**2 > radius**2:
# mask[row][col] = 0
# else:
# mask[row][col] = 255
img[:,:,0] = mask
self.frame = img
# for i in range(3):
# self.frame[:,:,i] = mask
#*********
if self.image_d == 1:
self.frame = rgb2gray(self.frame)
stop = datetime.now()
s = 1 / self.framerate - (stop - start).total_seconds()
if s > 0:
time.sleep(s)
self.cam.stop()
def update(self):
# keep looping infinitely until the thread is stopped
for f in self.stream:
# grab the frame from the stream and clear the stream in
# preparation for the next frame
self.frame = f.array
self.rawCapture.truncate(0)
if self.image_d == 1:
self.frame = rgb2gray(self.frame)
rgb_image = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB)
if self.dpcar_opt['org'] == True:
#### code edited @2020-07-06
cv2.imshow("ORIGIN Frame", rgb_image)
if self.mode == 'deepicar':
#hsv_image = self.lane_follower.cvtRGB2HSV(rgb_image) #BGR frame input
hsv_image = self.lane_follower.cvtBGR2HSV(
self.frame) #BGR frame input
edge_detected_image = self.lane_follower.detect_lane_edges(
hsv_image)
combo_image = self.lane_follower.detect_follow_lanes(
rgb_image, edge_detected_image)
if self.dpcar_opt['hsv'] == True:
cv2.imshow("BGR2HSV Image", hsv_image)
if self.dpcar_opt['edges'] == True:
cv2.imshow("Detected Edges Image", edge_detected_image)
if self.dpcar_opt['lanes'] == True:
cv2.imshow("Road with Lane line", combo_image)
#time.sleep(1)
cv2.waitKey(10)
#### code edited @2020-07-06
# if the thread indicator variable is set, stop the thread
if not self.on:
break
def update(self):
from datetime import datetime, timedelta
import pygame.image
while self.on:
start = datetime.now()
if self.cam.query_image():
# snapshot = self.cam.get_image()
# self.frame = list(pygame.image.tostring(snapshot, "RGB", False))
snapshot = self.cam.get_image()
snapshot1 = pygame.transform.scale(snapshot, self.resolution)
self.frame = pygame.surfarray.pixels3d(pygame.transform.rotate(pygame.transform.flip(snapshot1, True, False), 90))
if self.image_d == 1:
self.frame = rgb2gray(self.frame)
stop = datetime.now()
s = 1 / self.framerate - (stop - start).total_seconds()
if s > 0:
time.sleep(s)
self.cam.stop()
def update(self):
# keep looping infinitely until the thread is stopped
for f in self.stream:
# grab the frame from the stream and clear the stream in
# preparation for the next frame
self.frame = f.array
self.rawCapture.truncate(0)
#print('Captured %dx%dx%d image' % (self.frame.shape[1], self.frame.shape[0],self.frame.shape[2]))
self.detect()
'''
img = self.frame
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.imshow('image',img)
cv2.waitKey(30)
cv2.imwrite(str(self.num)+'traffic_sign.png',img)
print(self.num)
self.num = self.num + 1
'''
if self.image_d == 1:
self.frame = rgb2gray(self.frame)
# if the thread indicator variable is set, stop the thread
if not self.on:
break
def update(self):
from datetime import datetime, timedelta
import pygame.image
while self.on:
start = datetime.now()
# WebCamera: frame1
if self.cam.query_image():
snapshot = self.cam.get_image()
snapshot1 = pygame.transform.scale(snapshot, self.resolution)
frame1 = pygame.surfarray.pixels3d(
pygame.transform.rotate(pygame.transform.flip(snapshot1, True, False), -90))
# Import opencv for mask generation
greenLower = (30, 45, 70) # Set boundary for green (HSV Color Space)
greenUpper = (64, 255, 255)
img = frame1.copy() # Import image
blurred = cv2.GaussianBlur(img, (11, 11), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_RGB2HSV) # Transfer to HSV color space
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2) # Create mask to find green areas
mask = cv2.dilate(mask, None, iterations=2)
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if cnts[1] == []:
x, y, radius = [0, 0, 0]
else:
c = max(cnts[1], key=cv2.contourArea)
((y, x), radius) = cv2.minEnclosingCircle(c) # get center and radius of the ball
# img = cv2.circle(img, (int(y), int(x)), int(radius), (255, 0, 0), 2)
for row in range(mask.shape[0]):
for col in range(mask.shape[1]):
if (row-x)**2 + (col-y)**2 > radius**2:
mask[row][col] = 0
else:
mask[row][col] = 255
frame1 = rgb2gray(frame1)
# PiCamera: frame2
f = next(self.stream)
frame2 = rgb2gray(f.array)
self.rawCapture.truncate(0)
# Sensor info
Dist_M = np.zeros((self.image_h, self.image_w))
distance_in_mm1 = self.tof.get_distance(1)
distance_in_mm2 = self.tof.get_distance(2)
d1 = np.round(min(distance_in_mm1 / 1000 * 255, 255))
d2 = np.round(min(distance_in_mm2 / 1000 * 255, 255))
Dist_M[:, :int(self.image_w / 2)] = int(d1)
Dist_M[:, int(self.image_w / 2):] = int(d2)
# Fusion of both cameras, testing
# ??============================
self.fusion[:, :, 0] = frame2
self.fusion[:, :, 1] = mask
self.fusion[:, :, 2] = Dist_M
self.frame = self.fusion
# print("1: {}, 2: {}, reso: {}".format(frame1.dtype, frame2.dtype, self.resolution))
# =============================
stop = datetime.now()
s = 1 / self.framerate - (stop - start).total_seconds()
if s > 0:
time.sleep(s)
self.cam.stop()
def poll(self):
self.frame = self.cap.read()
if self.image_d == 1:
self.frame = rgb2gray(self.frame)
