def main():
drone = tellopy.Tello()
try:
drone.connect()
drone.wait_for_connection(60.0)
container = av.open(drone.get_video_stream())
while True:
frame = container.decode(video=0).next()
image = cv2.cvtColor(numpy.array(frame.to_image()), cv2.COLOR_RGB2BGR)
cv2.imshow('Original', image)
cv2.imshow('Canny', cv2.Canny(image, 100, 200))
cv2.waitKey(1)
except Exception as ex:
print(ex)
finally:
drone.quit()
cv2.destroyAllWindows()
while True:
ret, frame_vga = videoIn.read()
#ret2, frame2 = camera2.read()
frame1 = frame_vga
frame2 = frame_vga
if not ret:
break
# 根据更正map对图片进行重构
img1_rectified = cv2.remap(frame1, left_map1, left_map2, cv2.INTER_LINEAR)
img2_rectified = cv2.remap(frame2, right_map1, right_map2,
cv2.INTER_LINEAR)
# 将图片置为灰度图，为StereoBM作准备
imgL = cv2.cvtColor(img1_rectified, cv2.COLOR_BGR2GRAY)
imgR = cv2.cvtColor(img2_rectified, cv2.COLOR_BGR2GRAY)
#imgGrayL = cv2.equalizeHist(imgL)
#imgGrayR = cv2.equalizeHist(imgR)
# through gausiann filter
imgGrayL = cv2.GaussianBlur(imgL, (5, 5), 0) #高斯滤波
imgGrayR = cv2.GaussianBlur(imgR, (5, 5), 0)
# 两个trackbar用来调节不同的参数查看效果
# num = cv2.getTrackbarPos("num", "depth")
# blockSize = cv2.getTrackbarPos("blockSize", "depth")
# if blockSize % 2 == 0:
# blockSize += 1
# if blockSize < 5:
# blockSize = 5
font = cv2.FONT_HERSHEY_SIMPLEX
#iniciate id counter
id = 0
# names related to IDs
names = ['None', 'Nirmal', 'Raj', 'Lochan', 'Gita', 'Ramesh']
webcam = cv2.VideoCapture(0)
while True:
#Read current frame/picture
successful_frame_read, frame = webcam.read()
# Must convert images to grey-scale
grayscaled_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces
face_coordinates = trained_face_data.detectMultiScale(grayscaled_img,
scaleFactor=1.2,
minNeighbors=5,
minSize=(20, 20))
for (x, y, w, h) in face_coordinates:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255), 2)
id, confidence = face_recognizer.predict(grayscaled_img[y:y + h,
x:x + w])
if (confidence < 100):
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
else:
smile_detector = cv2.CascadeClassifier('data/haarcascade_smile.xml')
def detect(gray, frame):
faces = face_detector.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), ((x + w), (y + h)), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
smiles = smile_detector.detectMultiScale(roi_gray, 1.8, 20)
for (sx, sy, sw, sh) in smiles:
cv2.rectangle(roi_color, (sx, sy), ((sx + sw), (sy + sh)),
(0, 0, 255), 2)
return frame
video_capture = cv2.VideoCapture(0)
while True:
_, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
canvas = detect(gray, frame)
cv2.imshow('video', canvas)
if cv2.waitKey(1) & 0xff == ord('q'):
break
if cv2.getWindowProperty('video', 1) == -1:
break
video_capture.release()
cv2.destroyAllWindows()
import numpy as np
from cv2 import cv2
from matplotlib import pyplot as plt
imgL = cv2.imread('resource/scene_l.jpg')
imgR = cv2.imread('resource/scene_r.jpg')
imgL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
imgR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(imgL, imgR)
plt.imshow(disparity, 'gray')
plt.show()
print(e)
continue
global keep_going
keep_going = True
time_of_last_detect = time.time()
time_before_search = 1000
while keep_going:
for frame in container.decode(video=0):
if frame_skip > 0:
frame_skip -= 1
continue
start_time = time.time()
image = cv2.cvtColor(numpy.array(frame.to_image()), cv2.COLOR_RGB2BGR)
# resizing
height, width, _ = image.shape
image = cv2.resize(image, (int(720 * width / height), 720))
height, width, _ = image.shape
# human detector part
boxes, scores, classes, num = odapi.processFrame(image)
# displaying bounding boxes amd midpoint dot
for i in range(len(boxes)):
# Class 1 represents human
if classes[i] == 1 and scores[i] > threshold:
drone.clockwise(0)
time_of_last_detect = time.time()
def ChangeImage(self, image):
res = cv2.resize(image, (640, 480))
img = cv2.cvtColor(res, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img)
self.src = ImageTk.PhotoImage(image=img)
self.frame['image'] = self.src
def PILToCV2(image):
"""
:param image: PIL image object
:return: numpy array (cv2 accpet)
"""
return cv.cvtColor(np.asarray(image), cv.COLOR_LAB2BGR)
def _extract_hsl_value(image_path):
image = cv2.imread(image_path)
hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
h, l, s = cv2.split(hls)
return np.mean(h), np.mean(s), np.mean(l)
def _extract_hsv_value(image_path):
image = cv2.imread(image_path)
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
return np.mean(h), np.mean(s), np.mean(v)
def process(im):
global _old_x
global _old_y
global _old_w
global _old_h
global _old_lostsight
#
# Convert the image to HSV and then apply Thresholding - OpenCV standard is Blue-Green-Red
#
out = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
#
out = cv2.inRange(
out,
(_hsv_threshold_hue[0], _hsv_threshold_sat[0], _hsv_threshold_val[0]),
(_hsv_threshold_hue[1], _hsv_threshold_sat[1], _hsv_threshold_val[1]))
#
# masking out the area that we wanted by just taking an entire slice of the area and leave it in the 'out' image
out = out[_y1:_y2, _x1:_x2]
# also draw this slice on the original image in Red (BGR)
cv2.rectangle(im, _mask_p1, _mask_p2, (0, 0, 255), 1)
# Now, let's find the contour - we only keeping/using contours for this time
contours, _ = cv2.findContours(out,
mode=cv2.RETR_EXTERNAL,
method=cv2.CHAIN_APPROX_SIMPLE)
# still need to do a 'STOP' condition check before going into this loop...but for now, skip that
if len(contours) > 0:
# Line Merging Test
# locate the largest contour - for now, we only going to use/deal with the largest contours - kinda big assumption for now
c = max(contours, key=cv2.contourArea)
# And then find the bounding rectangle that fit this contours...this is going to enclosed everything that fits inside a rect.
# - will be put in x, y, width and height [_x, _y, _w and _h]
cb_x, cb_y, cb_w, cb_h = cv2.boundingRect(c)
# put this into the 'current or old' variables
_old_x = cb_x
_old_y = cb_y
_old_w = cb_w
_old_z = cb_h
_old_lostsight = 0
else:
# couldn't find any contours, for now, we just assumed to use the last one - note - need to perform the stop condition
cb_x = _old_x
cb_y = _old_y
cb_w = _old_w
cb_h = _old_h
_old_lostsight = _old_lostsight + 1
# the new bounding box = contains the min/max of the targetted/tracked line point
# cbbox = out[cb_y:cb_y+cb_h, cb_x:cb_x+cb_w]
# Now, draw this new bounding box on the original image in GREEN
cv2.rectangle(im, (cb_x, cb_y + _y1), (cb_x + cb_w, cb_y + cb_h + _y1),
(0, 255, 0), 1)
sp = genMask(out, (cb_x, cb_y), (cb_x + cb_w, cb_y + cb_h))
#cv2.rectangle(out, (cb_x, cb_y), (cb_x + cb_w, cb_y + cb_h), (0, 255, 255), 1)
#print(contours)
# debugging use - comment out for production
# cv2.imshow("Filtered", out)
# return out
## return cbbox
return sp
import sys
import numpy as np
from cv_helpers import show_compared_imgs, plt_show_img
from binarization import otsu_binarization, mask_binarization
from edge_detection import apply_prewitt
from cv2 import cv2
if __name__ == '__main__':
try:
img_path = sys.argv[1]
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
binarized_img = otsu_binarization(img)
edges_img = apply_prewitt(img)
normalized_mask = mask_binarization(
cv2.cvtColor(img, cv2.COLOR_RGB2GRAY))
binary_mask = edges_img * normalized_mask
show_compared_imgs(edges_img, binarized_img, binary_mask,
'Edges, Binary, Mask')
except Exception as error:
print(error)
k = cv.waitKey(0)
if k == 27:
cv.destroyAllWindows()
def nothing(x):
pass
cv.namedWindow('win', cv.WINDOW_NORMAL)
# 创建滑动条
bar = cv.createTrackbar('equ', 'win', 0, 10, nothing)
img = cv.imread('image/hist.jpg', 0)
while True:
x = cv.getTrackbarPos('equ', 'win')
# 进行均衡化
clahe = cv.createCLAHE(clipLimit=x, tileGridSize=(8, 8))
cl = clahe.apply(img)
cv.imshow('win', cl)
key = cv.waitKey(10)
if (key == 27):
break
# 2D直方图展示
img = cv.imread('image/hist.jpg')
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
hist = cv.calcHist([hsv], [0, 1], None, [150, 256], [0, 180, 0, 256])
plt.imshow(hist, interpolation='nearest')
plt.show()
result_image = res.reshape((img.shape))
figure_size = 15
plt.figure(figsize=(figure_size / 2, figure_size / 2))
plt.subplot(1, 2, 1), plt.imshow(img)
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(1, 2, 2), plt.imshow(result_image)
plt.title('Segmented Image when K = %i' % K), plt.xticks([]), plt.yticks(
[])
plt.show()
original_image = cv2.imread(
"2-venice-landmark-burano-island-canal-colorful-houses-and-boats-stevanzz-photography.jpg"
)
orig = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
coord = crop_half(orig)
start_row = coord[0]
end_row = coord[1]
start_col = coord[2]
end_col = coord[3]
# Training image
img = orig[start_row:end_row, start_col:end_col]
# Testing image
greyimg = orig[coord[4]:coord[5], coord[6]:coord[7]]
greyimg = cv2.cvtColor(greyimg, cv2.COLOR_BGR2GRAY)
greyimg = cv2.cvtColor(greyimg, cv2.COLOR_GRAY2BGR)
from __future__ import print_function
import cv2.cv2 as cv
import argparse
valorMedio = 127
valorMaximo = 255
window_name = 'Saida Limiar'
src = cv.imread('ancora.jpg')
if src is None:
print('Erro ao abrir o arquivo!')
exit(0)
src_cinza = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
dst = cv.adaptiveThreshold(src_cinza, valorMaximo,
cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 11,
2)
for i in range(0, 9):
dst = cv.adaptiveThreshold(dst, valorMaximo, cv.ADAPTIVE_THRESH_GAUSSIAN_C,
cv.THRESH_BINARY, 11, 2)
cv.imshow(window_name, dst)
cv.waitKey()
'''
max_value = 255
max_type = 4
max_binary_value = 255
trackbar_type = 'Type: \n 0: Binary \n 1: Binary Inverted \n 2: Truncate \n 3: To Zero \n 4: To Zero Inverted'
def CV2ToPIL(image):
"""
:param image: numpy array (cv2 accept)
:return: PIL image object
"""
return Image.fromarray(cv.cvtColor(image, cv.COLOR_BGR2LAB))
def _extract_Lab_value(image_path):
image = cv2.imread(image_path)
Lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
L, a, b = cv2.split(Lab)
return np.mean(L), np.mean(a), np.mean(b)
def MakeImg(self, path):
src = cv2.imread(path)
src = cv2.resize(src, (640, 480))
img = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img)
self.src = ImageTk.PhotoImage(image=img)
def convert_bga_to_rgb(self, input_image):
return cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
def main():
global star
global cascPath
posx = 0
counter = 0
drone = tellopy.Tello()
counter1 = 0
step = 200 #Seed Value, Dont Care :3
step1 = 200
stop = 0
flag1 = True
while star == 1:
cc = str(ser.readline())
if cc[2:][:-5] == "Calling Drone":
print(cc[2:][:-5])
star = 2
break
try:
#Start Protocol
drone.subscribe(drone.EVENT_FLIGHT_DATA, handler)
drone.connect()
drone.wait_for_connection(60.0)
container = av.open(drone.get_video_stream())
frame_skip = 300
xdis = 200
ydis = 150
drone.takeoff()
sleep(5)
drone.up(18)
sleep(5)
drone.up(0)
sleep(1)
countface = 0
# End Start Protocol
while True:
for frame in container.decode(video=0):
if 0 < frame_skip:
frame_skip = frame_skip - 1
continue
start_time = time.time()
image = cv2.cvtColor(numpy.array(frame.to_image()),
cv2.COLOR_RGB2BGR)
#imagep= cv2.cvtColor(numpy.array(frame.to_image()), cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
image,
scaleFactor=1.7,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
if (len(faces) == 0):
countface = 0
if (step != 0):
if (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.up(0)
drone.right(0)
elif (step == 8):
drone.up(0)
drone.left(0)
elif (step == 9):
drone.down(0)
drone.right(0)
elif (step == 10):
drone.down(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 0
counter1 += 1
if counter1 >= 10 and flag1 == True:
flag1 = False
if posx < 430:
drone.counter_clockwise(0)
else:
drone.clockwise(0)
else:
flag1 = True
counter1 = 0
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h),
(0, 255, 0), 2)
place = image[y:y + h, x:x + w]
#Save last X position in Memory
if (countface > 3):
posx = (x + (w / 2))
else:
countface += 1
signal = signalCascade.detectMultiScale(
image[y:y + h, x:x + w],
scaleFactor=3.5,
minNeighbors=20,
minSize=(20, 20),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in signal:
cv2.rectangle(place, (x, y), (x + w, y + h),
(255, 255, 0), 2)
if (counter == 10):
...
#raise ValueError('Close Connection')
#break
elif (len(signal) != 0):
counter += 1
else:
counter = 0
if (w * h > 60000 and step != 1):
if (step == 0):
drone.clockwise(0)
elif (step == 1):
...
#drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
drone.counter_clockwise(10)
cascPath = cascPathStop
stop = 1
step = 1
elif (w * h > 40000 and step != 2):
if (step != 2):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
if (stop == 1):
raise ValueError('Close Connection')
break
if (y < ydis and step1 != 1 and x > xdis
and (960 - x - w) > xdis):
step1 = 1
drone.up(12)
print("Up Stable")
elif ((720 - y - h) < ydis and step1 != 2
and x > xdis and (960 - x - w) > xdis):
step1 = 2
drone.down(12)
print("Down Stable")
elif (x < xdis and step1 != 3
and (720 - y - h) > ydis and y > ydis):
step1 = 3
drone.left(12)
print("Left Stable")
elif ((960 - x - w) < xdis and step1 != 4
and (720 - y - h) > ydis and y > ydis):
step1 = 4
drone.right(12)
print("Right Stable")
elif (y < ydis and step1 != 5 and x > xdis
and (960 - x - w) < xdis):
step1 = 5
drone.right(12)
drone.up(12)
print("Right Up Stable")
elif ((720 - y - h) < ydis and step1 != 6
and x > xdis and (960 - x - w) < xdis):
step1 = 6
drone.right(12)
drone.down(12)
print("Right Down Stable")
elif (y < ydis and step1 != 7 and x < xdis
and (960 - x - w) > xdis):
step1 = 7
drone.left(12)
drone.up(12)
print("Left Up Stable")
elif ((720 - y - h) < ydis and step1 != 8
and x < xdis and (960 - x - w) > xdis):
step1 = 8
drone.left(12)
drone.down(12)
print("Left Down Stable")
elif (step1 != 9 and (720 - y - h) > ydis
and y > ydis and x > xdis
and (960 - x - w) > xdis):
if (step1 == 1):
drone.up(0)
elif (step1 == 2):
drone.down(0)
elif (step1 == 3):
drone.left(0)
elif (step1 == 4):
drone.right(0)
elif (step1 == 5):
drone.up(0)
drone.right(0)
elif (step1 == 6):
drone.right(0)
drone.down(0)
elif (step1 == 7):
drone.left(0)
drone.up(0)
elif (step1 == 8):
drone.left(0)
drone.down(0)
else:
... #Nothing
step1 = 9
print("Stable")
else:
... #Nothing
step = 2
elif (y < ydis and step != 3 and x > xdis
and (960 - x - w) > xdis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 3
drone.up(12)
print("Up")
elif ((720 - y - h) < ydis and step != 4 and x > xdis
and (960 - x - w) > xdis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
...
#drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 4
drone.down(12)
print("Down")
elif (x < xdis and step != 5 and (720 - y - h) > ydis
and y > ydis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
...
#drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
print(step)
step = 5
drone.left(12)
print("Left")
elif ((960 - x - w) < xdis and step != 6
and (720 - y - h) > ydis and y > ydis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
...
#drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 6
drone.right(12)
print("Right")
elif ((960 - x - w) < xdis and step != 7
and (720 - y - h) < ydis and y > ydis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
...
#drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
...
#drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 7
drone.down(12)
drone.right(12)
print("Right Down")
elif (x < xdis and step != 8 and (720 - y - h) < ydis
and y > ydis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
...
#drone.down(0)
elif (step == 5):
...
#drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 8
drone.down(12)
drone.left(12)
print("Left Down")
elif ((960 - x - w) < xdis and step != 9
and (720 - y - h) > ydis and y < ydis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
...
#drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
...
#drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
...
#drone.up(0)
#drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 9
drone.up(12)
drone.right(12)
print("Right Up")
elif (x < xdis and step != 10 and (720 - y - h) > ydis
and y < ydis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
...
#drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
...
#drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
...
#drone.up(0)
#drone.left(0)
elif (step == 11):
drone.forward(0)
else:
... #Nothing
step = 10
drone.up(12)
drone.left(12)
print("Left Up")
elif (step != 11 and (720 - y - h) > ydis and y > ydis
and x > xdis and (960 - x - w) > xdis):
if (step == 0):
drone.counter_clockwise(0)
drone.clockwise(0)
elif (step == 1):
drone.backward(0)
elif (step == 3):
drone.up(0)
elif (step == 4):
drone.down(0)
elif (step == 5):
drone.left(0)
elif (step == 6):
drone.right(0)
elif (step == 7):
drone.down(0)
drone.right(0)
elif (step == 8):
drone.down(0)
drone.left(0)
elif (step == 9):
drone.up(0)
drone.right(0)
elif (step == 10):
drone.up(0)
drone.left(0)
elif (step == 11):
...
#drone.forward(0)
else:
... #Nothing
step = 11
drone.forward(12)
print("Adelante")
else:
... #Nothing
# Display the resulting frame
cv2.putText(image, "Battery %:" + str(battery), (10, 30), font,
1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.imshow('Original', image)
if cv2.waitKey(1) & 0xFF == ord('q'):
raise ValueError('Close Connection')
break
elif frame.time_base < 1.0 / 60:
time_base = 1.0 / 60
else:
time_base = frame.time_base
frame_skip = int((time.time() - start_time) / time_base)
except:
drone.forward(0)
drone.backward(0)
drone.right(0)
drone.left(0)
drone.down(0)
drone.up(0)
drone.counter_clockwise(100)
drone.land()
sleep(5)
drone.quit()
cv2.destroyAllWindows()
exit()
vid = cv2.VideoCapture(
filename) #get video by input's filename.
total = int(vid.get(cv2.CAP_PROP_FRAME_COUNT)
) #counts the total number of video's frames.
fourcc = cv2.VideoWriter_fourcc(*'XVID')
outputfile = input("\nGive the file name of the output video: "
) #name the output video.
out = cv2.VideoWriter(outputfile + '.avi', fourcc, vid.get(5),
(int(vid.get(3)), int(vid.get(4))),
False)
start = time.time() #time calculation
print("\nCreating output video with name %s.avi..." %
outputfile) #information message.
playing, ref = vid.read()
ref = cv2.cvtColor(ref, cv2.COLOR_BGR2GRAY) #grayscale
#fill the black pixels.
ref = np.pad(
ref,
((0, int((np.ceil(vid.get(4) / 16) * 16) - vid.get(4))),
(0, int((np.ceil(vid.get(3) / 16) * 16) - vid.get(3)))),
'constant',
constant_values=0)
array = [] #entropy array.
print("Frame #1 of " + str(total) +
" Completed.") #information message.
#whlie loop statement for the next frames.
while vid.isOpened():
playing, frame = vid.read()
cnt = x[0]
return cnt
cap = cv2.VideoCapture(0)
while 1:
_, frame = cap.read()
frame = cv2.flip(frame, 1)
roi = frame[50:350, 200:400] #[y1:y2,x1:x2]
cv2.rectangle(frame, (200, 50), (400, 350), (0, 0, 255), 0)
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
lower_color = np.array([0, 45, 79], dtype=np.uint8)
upper_color = np.array([17, 255, 255], dtype=np.uint8)
mask = cv2.inRange(hsv, lower_color, upper_color)
kernel = np.ones((3, 3), np.uint8)
mask = cv2.dilate(mask, kernel, iterations=1)
mask = cv2.medianBlur(mask, 15)
contours, _ = cv2.findContours(mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
try:
c = findMaxContour(contours)
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
def OpenCV():
retry = 3
container = None
while container is None and 0 < retry:
retry -= 1
try:
# 動画の受信開始を処理
container = av.open(drone.get_video_stream()
) # container = tello video 映像の圧縮データを展開
except av.AVError as ave:
print(ave)
print('retry...')
frame_skip = 300 #動画接続前
while True:
for frame in container.decode(video=0): # .decode byte(映像の中身) -> 文字列
if 0 < frame_skip: #フレームスキップ処理
frame_skip = frame_skip - 1
continue
start_time = time.time()
image_origin = cv2.cvtColor(np.array(frame.to_image()),
cv2.COLOR_RGB2BGR) #RGB convert
h, w, c = image_origin.shape
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
r = image[:, :, 2]
g = image[:, :, 1]
b = image[:, :, 0]
R = np.array(r).flatten()
G = np.array(g).flatten()
B = np.array(b).flatten()
#R = [x for x in R if x > 15]
#G = [x for x in G if x > 15]
#B = [x for x in B if x > 15]
V1 = np.std(R)
V2 = np.std(G)
V3 = np.std(B)
mode = sstats.mode(R)[0]
mode1 = sstats.mode(G)[0]
mode2 = sstats.mode(B)[0]
threshold_img = image.copy()
threshold_img[r < mode1 - 3.7 * V1] = 0
threshold_img[r >= mode1 - 3.7 * V1] = 255
threshold_img[r > mode1 + 3.7 * V1] = 0
threshold_img[g > mode2 - 3.7 * V2] = 0
#feature_params = {"maxCorners": 4, "qualityLevel": 0.3, "minDistance": 30, "blockSize": 12}
#feature_params = {"maxCorners": 8, "qualityLevel": 0.3, "minDistance": 10, "blockSize": 12}
feature_params = {
"maxCorners": 12,
"qualityLevel": 0.3,
"minDistance": 5,
"blockSize": 9
}
#feature_params = {"maxCorners": 4, "qualityLevel": 0.3, "minDistance": 5, "blockSize": 9}
#特徴点の上限数 # 閾値 （高いほど特徴点数は減る) # 特徴点間の距離 (近すぎる点は除外)
image2 = cv2.blur(image, (3, 3))
A = np.uint8(image[:, :, 2])
image2 = cv2.blur(threshold_img, (3, 3))
A = np.uint8(image2[:, :, 2])
p0 = cv2.goodFeaturesToTrack(A, mask=None, **feature_params)
for p in p0: #p0 x,y zahyou 3image_origin
x, y = p.ravel() #p0 no youso wo bunkai
cv2.circle(image, (x, y), 5, (0, 255, 255), -1)
"""
cv2.imshow("image", image)
cv2.imshow("image1", image1)
cv2.imshow("image2", image2)
cv2.imshow("image_thresh", threshold_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
x0 = p0[:, :, 0].ravel() #x座標
y0 = p0[:, :, 1].ravel() #y座標
MX = max(x0)
mx = min(x0)
MY = max(y0)
my = min(y0)
avex = (MX + mx) / 2
avey = (MY + my) / 2
"""
print(x0)
print(y0)
print(MX)
print(mx)
print(MY)
print(my)
print(avex)
print(avey)
print(type(avex))
print(type(x0[0]))
"""
MX = int(MX)
mx = int(mx)
MY = int(MY)
my = int(my)
avex = int(avex)
avey = int(avey)
a = [0] * 12
b = [0] * 12
x1 = []
y1 = []
for i in range(len(x0)):
if y0[i] < avey:
x1.append(x0[i])
y1.append(y0[i])
l11 = np.sqrt((x1[0])**2 + (y1[0])**2)
l21 = np.sqrt((x1[1])**2 + (y1[1])**2)
l31 = np.sqrt((x1[2])**2 + (y1[2])**2)
l41 = np.sqrt((x1[3])**2 + (y1[3])**2)
l1 = [l11, l21, l31, l41]
print(l1)
c = [0, 1, 2, 3]
for i in range(len(l1)):
if l1[i] == min(l1):
a[0] = x1[i]
b[0] = y1[i]
s = i
c.remove(s)
j = 0
for j in c:
n = c.copy()
A = (b[0] - y1[j]) / (a[0] - x1[j])
B = b[0] - A * a[0]
n.remove(j)
C = A * x1[n[0]] + B
D = A * x1[n[1]] + B
if C - y1[n[0]] > 0 and D - y1[n[1]] < 0:
a[1] = x1[n[0]]
b[1] = y1[n[0]]
a[3] = x1[n[1]]
b[3] = y1[n[1]]
a[2] = x1[j]
b[2] = y1[j]
break
elif C - y1[n[0]] < 0 and D - y1[n[1]] > 0:
a[3] = x1[n[0]]
b[3] = y1[n[0]]
a[1] = x1[n[1]]
b[1] = y1[n[1]]
a[2] = x1[j]
b[2] = y1[j]
break
d1 = np.sqrt((a[0] - a[1])**2 + (b[0] - b[1])**2)
d2 = np.sqrt((a[1] - a[2])**2 + (b[1] - b[2])**2)
d3 = np.sqrt((a[2] - a[3])**2 + (b[2] - b[3])**2)
d4 = np.sqrt((a[3] - a[0])**2 + (b[3] - b[0])**2)
line1 = cv2.line(image, (a[0], b[0]), (a[1], b[1]), 100)
line2 = cv2.line(image, (a[1], b[1]), (a[2], b[2]), 100)
line3 = cv2.line(image, (a[2], b[2]), (a[3], b[3]), 100)
line4 = cv2.line(image, (a[3], b[3]), (a[0], b[0]), 100)
x2 = []
y2 = []
for i in range(len(x0)):
if y0[i] > avey and x0[i] < avex:
x2.append(x0[i])
y2.append(y0[i])
l12 = np.sqrt((x2[0])**2 + (y2[0])**2)
l22 = np.sqrt((x2[1])**2 + (y2[1])**2)
l32 = np.sqrt((x2[2])**2 + (y2[2])**2)
l42 = np.sqrt((x2[3])**2 + (y2[3])**2)
l2 = [l12, l22, l32, l42]
print(l2)
d = [0, 1, 2, 3]
for i in range(len(l2)):
if l2[i] == min(l2):
a[4] = x2[i]
b[4] = y2[i]
s = i
d.remove(s)
k = 0
for k in d:
n = d.copy()
A = (b[4] - y2[k]) / (a[4] - x2[k])
B = b[4] - A * a[4]
n.remove(k)
C = A * x2[n[0]] + B
D = A * x2[n[1]] + B
if C - y2[n[0]] > 0 and D - y2[n[1]] < 0:
a[5] = x2[n[0]]
b[5] = y2[n[0]]
a[7] = x2[n[1]]
b[7] = y2[n[1]]
a[6] = x2[k]
b[6] = y2[k]
break
elif C - y2[n[0]] < 0 and D - y2[n[1]] > 0:
a[5] = x2[n[0]]
b[5] = y2[n[0]]
a[6] = x1[n[1]]
b[6] = y1[n[1]]
a[7] = x1[k]
b[7] = y1[k]
break
d4 = np.sqrt((a[4] - a[5])**2 + (b[4] - b[5])**2)
d5 = np.sqrt((a[5] - a[6])**2 + (b[5] - b[6])**2)
d6 = np.sqrt((a[6] - a[7])**2 + (b[6] - b[7])**2)
d7 = np.sqrt((a[7] - a[4])**2 + (b[7] - b[4])**2)
line4 = cv2.line(image, (a[4], b[4]), (a[5], b[5]), 100)
line5 = cv2.line(image, (a[5], b[5]), (a[6], b[6]), 100)
line6 = cv2.line(image, (a[6], b[6]), (a[7], b[7]), 100)
line7 = cv2.line(image, (a[7], b[7]), (a[4], b[4]), 100)
x3 = []
y3 = []
for i in range(len(x0)):
if y0[i] > avey and x0[i] > avex:
x3.append(x0[i])
y3.append(y0[i])
l13 = np.sqrt((x3[0])**2 + (y3[0])**2)
l23 = np.sqrt((x3[1])**2 + (y3[1])**2)
l33 = np.sqrt((x3[2])**2 + (y3[2])**2)
l43 = np.sqrt((x3[3])**2 + (y3[3])**2)
l3 = [l13, l23, l33, l43]
print(l3)
e = [0, 1, 2, 3]
for i in range(len(l3)):
if l3[i] == min(l3):
a[8] = x3[i]
b[8] = y3[i]
s = i
e.remove(s)
z = 0
for z in e:
n = e.copy()
A = (b[8] - y3[z]) / (a[8] - x3[z])
B = b[8] - A * a[8]
n.remove(z)
C = A * x3[n[0]] + B
D = A * x3[n[1]] + B
#if C - y3[n[0]] > 0 and D - y3[n[1]] < 0:
if C - y3[n[0]] < 0 and D - y3[n[1]] > 0:
a[9] = x3[n[0]]
b[9] = y3[n[0]]
a[11] = x3[n[1]]
b[11] = y3[n[1]]
a[10] = x3[z]
b[10] = y3[z]
break
#elif C - y3[n[0]] < 0 and D - y3[n[1]] > 0:
elif C - y3[n[0]] > 0 and D - y3[n[1]] < 0:
a[9] = x3[n[0]]
b[9] = y3[n[0]]
a[10] = x3[n[1]]
b[10] = y3[n[1]]
a[11] = x3[z]
b[11] = y3[z]
break
d8 = np.sqrt((a[8] - a[9])**2 + (b[8] - b[9])**2)
d9 = np.sqrt((a[9] - a[10])**2 + (b[9] - b[10])**2)
d10 = np.sqrt((a[10] - a[11])**2 + (b[10] - b[11])**2)
d11 = np.sqrt((a[11] - a[8])**2 + (b[11] - b[8])**2)
line8 = cv2.line(image, (a[8], b[8]), (a[9], b[9]), 100)
line9 = cv2.line(image, (a[9], b[9]), (a[10], b[10]), 100)
line10 = cv2.line(image, (a[10], b[10]), (a[11], b[11]), 100)
line11 = cv2.line(image, (a[11], b[11]), (a[8], b[8]), 100)
"""
print(avex)
print(avey)
print(x0)
print(y0)
print('x1[0]:%d' % x1[0])
print('y1[0]:%d' % y1[0])
print('x1[1]:%d' % x1[1])
print('y1[1]:%d' % y1[1])
print('x1[2]:%d' % x1[2])
print('y1[2]:%d' % y1[2])
print('x1[3]:%d' % x1[3])
print('y1[3]:%d' % y1[3])
print('a[0]:%d' % a[0])
print('b[0]:%d' % b[0])
print('a[1]:%d' % a[1])
print('b[1]:%d' % b[1])
print('a[2]:%d' % a[2])
print('b[2]:%d' % b[2])
print('a[3]:%d' % a[3])
print('b[3]:%d' % b[3])
print('a[4]:%d' % a[4])
print('b[4]:%d' % b[4])
print('a[5]:%d' % a[5])
print('b[5]:%d' % b[5])
print('a[6]:%d' % a[6])
print('b[6]:%d' % b[6])
print('a[7]:%d' % a[7])
print('b[7]:%d' % b[7])
print('a[8]:%d' % a[8])
print('b[8]:%d' % b[8])
print('a[9]:%d' % a[9])
print('b[9]:%d' % b[9])
print('a[10]:%d' % a[10])
print('b[10]:%d' % b[10])
print('a[11]:%d' % a[11])
print('b[11]:%d' % b[11])
cv2.imshow("image", image)
cv2.imshow("thresh", threshold_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
c1 = (a[0] + a[2]) / 2
c2 = (b[0] + b[2]) / 2
c3 = (a[4] + a[6]) / 2
c4 = (b[4] + b[6]) / 2
c5 = (a[8] + a[10]) / 2
c6 = (b[8] + b[10]) / 2
print(x0)
print(y0)
c11 = int(c1)
c12 = int(c2)
c13 = int(c3)
c14 = int(c4)
c15 = int(c5)
c16 = int(c6)
#line1 = cv2.line(image,(c11,c12),(c13,c14),100)
line = cv2.line(image, (c13, c14), (c15, c16), 100)
#line3 = cv2.line(image,(c15,c16),(c11,c12),100)
D = np.sqrt((c3 - c5)**2 + (c4 - c6)**2)
Tx = (c3 + c5) / 2
Ty = (c4 + c6) / 2
Da = (c4 - c6) / (c3 - c5)
DDa = -1 / Da
Dx = (c6 - Da * c4 + DDa * c1 - c2) / (DDa - Da)
Dy = Da * Dx + c6 - Da * c4
#lineT = cv2.line(image,(int(Tx),int(Ty)),(c11,c12),100)
lineDT = cv2.line(image, (int(Dx), int(Dy)), (c11, c12), 100)
T = np.sqrt((Tx - c1)**2 + (Ty - c2)**2)
DD = np.sqrt((Dx - c1)**2 + (Dy - c2)**2)
X = (T * 0.6) / D
XD = (DD * 0.6) / D
print(DD)
print(XD)
print(T)
print(D)
print(X)
"""
l1 = np.sqrt((c11-c13)**2 + (c12-c14)**2)
l2 = np.sqrt((c13-c14)**2 + (c15-c16)**2)
l3 = np.sqrt((c15-c16)**2 + (c11-c13)**2)
s = (l1 + l2 + l3) / 2
Sh = np.sqrt(s*(s-l1)*(s-l2)*(s-l3))
sin1 = Sh / (l1*l3)
theta1 = math.degrees(math.asin(sin1))
sin2 = Sh / (l2*l3)
theta2 = math.degrees(math.asin(sin2))
sin3 = Sh / (l2*l1)
theta3 = math.degrees(math.asin(sin3))
print(Sh)
print(l1)
print(l2)
print(l3)
print(theta1)
print(theta2)
print(theta3)
"""
S = abs((1 / 2) * ((a[3] - a[0]) * (b[1] - b[0]) - (a[1] - a[0]) *
(b[3] - b[0]))) + abs(
(1 / 2) * ((a[1] - a[2]) * (b[3] - b[2]) -
(a[3] - a[2]) * (b[1] - b[2])))
filename = 'telloimage' + str(frame) + '.jpg'
cv2.imwrite(filename, image_origin)
with open("S1 2021.3.10 8:19.txt", "a") as f:
result = "{:.7f}\n".format(S)
f.write(result)
with open("d1 2021.3.10 8:19..txt", "a") as f:
result = "{:.7f}\n".format(S)
f.write(result)
with open("d2 2021.3.10 8:19..txt", "a") as f:
result = "{:.7f}\n".format(d2)
f.write(result)
print(S)
print(d1)
print(d2)
cy = h / 2
cx = w / 2
data = [S, c1, c2, p0, cx, cy]
return data
if frame.time_base < 1.0 / 60:
time_base = 1.0 / 60 #機械のエラーを判別するための基準
else:
time_base = frame.time_base
#フレームスキップ値を算出
frame_skip = int((time.time() - start_time) / time_base)
import numpy as np
from cv2 import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
img = cv2.imread('image2.jpg')
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(grayImage, 1.1, 7)
if len(faces) == 0:
print("no faces found")
else:
print(faces)
print(faces.shape)
print("Number of faces detected: " + str(faces.shape[0]))
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
cv2.imshow('Image with faces', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def thread(self):
self.last_time = None
with self.playing_cv:
while self.running:
self.playing_cv.wait_for(lambda: self.playing)
if not self.running:
break
with self.capture_mutex:
rate = 1 / self.capture.get(cv2.CAP_PROP_FPS)
frame_index = round(
self.capture.get(cv2.CAP_PROP_POS_FRAMES))
remaining, frame = self.capture.read()
data = {
VideoInfoRow.Frame:
frame_index,
VideoInfoRow.Time:
self.capture.get(cv2.CAP_PROP_POS_MSEC),
VideoInfoRow.Progress:
self.capture.get(cv2.CAP_PROP_POS_AVI_RATIO),
}
width = round(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = round(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
if not remaining:
self.finished.emit(data)
break
rgb_image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w, ch = rgb_image.shape
bytes_per_line = ch * w
image = QImage(rgb_image.data, w, h, bytes_per_line,
QImage.Format_RGB888)
if not self.detector_enabled:
results = []
else:
with self.results_mutex:
if frame_index not in self.frames:
def valid_bbox(bbox):
return bbox[0] >= 0 and bbox[1] >= 0 and bbox[
2] < width and bbox[3] < height
# Get all instances
instances = [{
'instance_feature': instance['reid'],
'instance_bbox': instance['bbox'],
} for instance in self.reid_model.predict(frame)
if valid_bbox(instance['bbox'])]
if instances:
features = np.stack([
instance['instance_feature']
for instance in instances
])
similarity = cosine(features,
self.instance_features)
while similarity.size:
matched = np.unravel_index(
np.argmin(similarity),
similarity.shape)
if similarity[
matched] >= INSTANCE_SIMILARITY_THRESHOLD:
break
uuid = list(self.instances)[matched[1]]
instances[matched[0]]['instance'] = uuid
self.instance_features[matched[1]] = \
FEATURE_DECAY * self.instance_features[matched[1]] + \
(1 - FEATURE_DECAY) * features[matched[0]]
# self.instance_features[matched[1]] = np.average(np.stack(
# [instance['instance_feature'] for frame in self.frames.values()
# for instance in frame if instance['instance'] == uuid] +
# [features[matched[0]]]),
# axis=0)
similarity[matched[0], :] = np.full(
similarity.shape[1],
2) # 2 is largest similarity
similarity[:, matched[1]] = np.full(
similarity.shape[0], 2)
for instance in instances:
if 'instance' not in instance:
new_uuid = uuid4()
new_instance = {
'color': random_color(),
'name': None,
}
self.instances[new_uuid] = new_instance
instance['instance'] = new_uuid
self.instance_features = np.concatenate(
(self.instance_features,
instance['instance_feature'][
np.newaxis, :]))
faces_instance_index = []
for i, instance in enumerate(instances):
bbox = instance['instance_bbox']
instance_image = frame[bbox[1]:bbox[3],
bbox[0]:bbox[2]]
instance_image_rgb = cv2.cvtColor(
instance_image, cv2.COLOR_BGR2RGB)
attributes = self.attribute_model.predict(
Image.fromarray(
instance_image_rgb).convert('RGB'))
instance['attributes'] = attributes
faces = self.detect_model.predict(
instance_image)
if faces:
face_bbox = max(faces, key=lambda x: x[4])
face_bbox = [
round(face_bbox[0]),
round(face_bbox[1]),
round(face_bbox[2]),
round(face_bbox[3])
]
face_image = instance_image[
face_bbox[1]:face_bbox[3],
face_bbox[0]:face_bbox[2]]
face_image = cv2.cvtColor(
face_image, cv2.COLOR_BGR2RGB)
feature = self.recognize_model.predict_raw(
Image.fromarray(face_image)).squeeze(0)
faces_instance_index.append(i)
instance['face_feature'] = feature
instance['face_bbox'] = [
bbox[0] + face_bbox[0],
bbox[1] + face_bbox[1],
bbox[0] + face_bbox[2],
bbox[1] + face_bbox[3],
]
else:
instance['face'] = None
instance['face_feature'] = None
instance['face_bbox'] = None
if faces_instance_index:
features = np.stack([
instances[faces_instance_index]
['face_feature'] for faces_instance_index
in faces_instance_index
])
similarity = cosine(features,
self.face_features)
while similarity.size:
matched = np.unravel_index(
np.argmin(similarity),
similarity.shape)
if similarity[
matched] >= FACE_SIMILARITY_THRESHOLD:
break
uuid = list(self.faces)[matched[1]]
instances[faces_instance_index[
matched[0]]]['face'] = uuid
self.face_features[matched[1]] = FEATURE_DECAY * self.face_features[matched[1]] + \
(1 - FEATURE_DECAY) * features[matched[0]]
# self.face_features[matched[1]] = np.average(np.stack(
# [instance['face_feature'] for frame in self.frames.values()
# for instance in frame if instance['face'] == uuid] +
# [features[matched[0]]]),
# axis=0)
similarity[matched[0], :] = np.full(
similarity.shape[1],
2) # 2 is largest similarity
similarity[:, matched[1]] = np.full(
similarity.shape[0], 2)
for instance in instances:
if 'face' in instance or 'face_feature' not in instance:
continue
new_uuid = uuid4()
new_face = {
'color': random_color(),
'name': None,
}
self.faces[new_uuid] = new_face
instance['face'] = new_uuid
self.face_features = np.concatenate(
(self.face_features,
instance['face_feature'][np.newaxis, :]))
self.frames[frame_index] = instances
results = self.generate_results(frame_index)
new_time = time.time()
if self.last_time is not None and new_time < self.last_time + rate:
time.sleep(
self.last_time + rate - new_time
) # it is hard to sleep with a condition variable
self.last_time = new_time
self.frameReady.emit(image, data, results)
self.running = False
import cv2.cv2 as cv
import matplotlib.pyplot as plt
img = cv.imread("imgs/lena.jpg", -1)
img1 = cv.imread("imgs/orange.jpg", -1)
cv.imshow("original image", img)
# converting bgr 2 rgb
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
img1 = cv.cvtColor(img1, cv.COLOR_BGR2RGB)
# single image
plt.imshow(img)
# removing cordinate
plt.xticks([]), plt.yticks([])
plt.show()
# multiple image
plt.subplot(1, 2, 1), plt.imshow(img)
plt.title("first image")
plt.xticks([]), plt.yticks([])
plt.subplot(1, 2, 2), plt.imshow(img1)
plt.title("second image")
plt.xticks([]), plt.yticks([])
plt.show()
cv.waitKey(0)
cv.destroyAllWindows()
kamera = simulator
# wird zum Testen mit dem richtigen Roboter durch das AFMotorShield ersetzt
motoren = simulator
# CPU quälen:
while True:
# frame lesen
dieser_frame = kamera.get_frame()
####################################
# Summierung #
####################################
# Kopie von dieser_frame in Graustufen konvertieren
frame_graustufen = cv2.cvtColor(dieser_frame, cv2.COLOR_BGR2GRAY)
schwelle = 120
# alle Werte in dieser_frame bei Schwellenwert schwelle in 0 oder 255 zerteilen, Bild invertieren
_, linien_maske = cv2.threshold(frame_graustufen, schwelle, 255,
cv2.THRESH_BINARY_INV)
h = 50 # Höhe
b = 50 # Breite
start1 = (int(100), int(100))
ende1 = (int(start1[0] + b), int(start1[1] + h))
start2 = (int(200), int(100))
ende2 = (int(start2[0] + b), int(start2[1] + h))
# Rechteck von oben links (x=100, y=100) bis unten rechts (x=150, y=150) ausschneiden:
roi_links = linien_maske[start1[1]:ende1[1], start1[0]:ende1[0]]
def process_wheel():
global frame
wheel_frame = frame.copy()
hsv = cv2.cvtColor(wheel_frame, cv2.COLOR_BGR2HSV)
lower_blue = np.array(lb.copy()) # [35, 100, 0]
upper_blue = np.array(rb.copy()) # [255, 255, 255])
mask = cv2.inRange(hsv, lower_blue, upper_blue)
anded_res = cv2.bitwise_and(wheel_frame, wheel_frame, mask=mask)
contours, _ = cv2.findContours(cv2.Canny(anded_res, 255 / 3, 255),
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
area_threshold = 400
inds = []
for i, c in enumerate(contours):
a = cv2.contourArea(c)
if a > area_threshold and len(inds) < 2:
inds.append(i)
if not inds or len(inds) != 2:
cv2.imshow('wheel', wheel_frame) # [165:200, 326:500])
# cv2.imshow('mask', mask) # [165:200, 326:500])
return
if cv2.contourArea(contours[inds[0]]) < cv2.contourArea(contours[inds[1]]):
inds[0], inds[1] = inds[1], inds[0]
moments1 = cv2.moments(contours[inds[0]])
moments2 = cv2.moments(contours[inds[1]])
p1 = [
int(moments1["m10"] / moments1["m00"]),
int(moments1["m01"] / moments1["m00"])
]
p2 = [
int(moments2["m10"] / moments2["m00"]),
int(moments2["m01"] / moments2["m00"])
]
cv2.circle(wheel_frame, (p1[0], p1[1]), 3, (255, 255, 255), -1)
cv2.circle(wheel_frame, (p2[0], p2[1]), 3, (255, 255, 255), -1)
cv2.line(wheel_frame, (p1[0], p1[1]), (p2[0], p2[1]), (0, 255, 0), 2)
if p2[0] - p1[0] == 0:
slope = 90
else:
slope = -np.rad2deg(np.arctan2((p2[1] - p1[1]), (p2[0] - p1[0]))) % 360
cv2.drawContours(wheel_frame, contours, inds[0], (0, 0, 255), 2)
cv2.drawContours(wheel_frame, contours, inds[1], (0, 255, 0), 2)
cv2.putText(wheel_frame, "Steering angle: {}".format(np.round(slope)),
(10, 100), cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 0), 2)
cv2.putText(wheel_frame, "{}".format(get_action()), (10, 140),
cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 0), 2)
cv2.line(wheel_frame, (0, 200), (600, 200), (255, 255, 255), 1)
cv2.line(wheel_frame, (0, 250), (600, 250), (255, 255, 255), 1)
steer(slope)
gas(p1[1])
cv2.imshow('wheel', wheel_frame) # [165:200, 326:500])
import numpy as np
from cv2 import cv2 as cv
from color_grids_detection import DetectColorGrids
import os
TOTAL_COLUMNS = 6
TOTAL_ROWS = 4
CURRENT_DIR = os.getcwd()
img = cv.imread(CURRENT_DIR + '\\image\\image.jpg')
output_path = CURRENT_DIR + '\\image\\'
cascade_path = CURRENT_DIR + '\\cascade.xml'
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = cv.equalizeHist(gray)
cascade = cv.CascadeClassifier(cascade_path)
objects = cascade.detectMultiScale(gray, 1.1, 6, 1, (129, 90))
for single_obj in objects:
cv.rectangle(img, single_obj, (255, 255, 255))
x, y, w, h = single_obj
crop_img = img[y:y + h, x:x + w]
grids_position = np.zeros((TOTAL_ROWS, TOTAL_COLUMNS, 2), dtype=np.int)
grids_position = DetectColorGrids(crop_img)
row_count = 0
col_count = 0
while (row_count < TOTAL_ROWS):
col_count = 0
def get_sym_score(img):
height = int(img.shape[0])
width = int(img.shape[1])
cell_x = height // opt.cell_size[0]
cell_y = width // opt.cell_size[1]
# left-right
img_left = img[:, :width // 2]
img_right = img[:, width // 2:]
imgl_rs = np.split(img_left, cell_x, axis=0)
imgr_rs = np.split(img_right[:, ::-1], cell_x, axis=0)
histl = np.ndarray((cell_x, cell_y // 2, opt.rgbbins * 3))
histr = np.ndarray((cell_x, cell_y // 2, opt.rgbbins * 3))
for r in range(cell_x):
imgl_rc = np.split(imgl_rs[r], cell_y // 2, axis=1)
imgr_rc = np.split(imgr_rs[r], cell_y // 2, axis=1)
for c in range(cell_y // 2):
histl_r = np.histogram(imgl_rc[c][:, :, 0], opt.rgbbins,
(0, 255))[0]
histl_g = np.histogram(imgl_rc[c][:, :, 1], opt.rgbbins,
(0, 255))[0]
histl_b = np.histogram(imgl_rc[c][:, :, 2], opt.rgbbins,
(0, 255))[0]
histr_r = np.histogram(imgr_rc[c][:, :, 0], opt.rgbbins,
(0, 255))[0]
histr_g = np.histogram(imgr_rc[c][:, :, 1], opt.rgbbins,
(0, 255))[0]
histr_b = np.histogram(imgr_rc[c][:, :, 2], opt.rgbbins,
(0, 255))[0]
histl[r, c] = np.concatenate(
(histl_r, histl_g, histl_b)) / np.sum(histl_r) / 3
histr[r, c] = np.concatenate(
(histr_r, histr_g, histr_b)) / np.sum(histr_r) / 3
binsl = myHOG(cv.cvtColor(img_left, cv.COLOR_BGR2GRAY), cell_x,
cell_y // 2, opt.cell_size)
binsr = myHOG(cv.cvtColor(img_right[:, ::-1], cv.COLOR_BGR2GRAY), cell_x,
cell_y // 2, opt.cell_size)
cost_lr = np.sum(np.abs(histl - histr)) / cell_x / cell_y * 2
dist_hoglr = np.sum(np.abs(binsl - binsr)) / cell_x / cell_y * 2
# top-bottom
img_top = img[:height // 2]
img_bottom = img[height // 2:]
imgt_rs = np.split(img_top, cell_x // 2, axis=0)
imgb_rs = np.split(img_bottom[::-1], cell_x // 2, axis=0)
histt = np.ndarray((cell_x // 2, cell_y, opt.rgbbins * 3))
histb = np.ndarray((cell_x // 2, cell_y, opt.rgbbins * 3))
for r in range(cell_x // 2):
imgt_rc = np.split(imgt_rs[r], cell_y, axis=1)
imgb_rc = np.split(imgb_rs[r], cell_y, axis=1)
for c in range(cell_y):
histt_r = np.histogram(imgt_rc[c][:, :, 0], opt.rgbbins,
(0, 255))[0]
histt_g = np.histogram(imgt_rc[c][:, :, 1], opt.rgbbins,
(0, 255))[0]
histt_b = np.histogram(imgt_rc[c][:, :, 2], opt.rgbbins,
(0, 255))[0]
histb_r = np.histogram(imgb_rc[c][:, :, 0], opt.rgbbins,
(0, 255))[0]
histb_g = np.histogram(imgb_rc[c][:, :, 1], opt.rgbbins,
(0, 255))[0]
histb_b = np.histogram(imgb_rc[c][:, :, 2], opt.rgbbins,
(0, 255))[0]
histt[r, c] = np.concatenate(
(histt_r, histt_g, histt_b)) / np.sum(histt_r) / 3
histb[r, c] = np.concatenate(
(histb_r, histb_g, histb_b)) / np.sum(histb_r) / 3
binst = myHOG(cv.cvtColor(img_top, cv.COLOR_BGR2GRAY), cell_x // 2, cell_y,
opt.cell_size)
binsb = myHOG(cv.cvtColor(img_bottom[::-1], cv.COLOR_BGR2GRAY),
cell_x // 2, cell_y, opt.cell_size)
cost_tb = np.sum(np.abs(histt - histb)) / cell_x / cell_y * 2
dist_hogtb = np.sum(np.abs(binst - binsb)) / cell_x / cell_y * 2
return np.array([cost_lr, dist_hoglr, cost_tb, dist_hogtb])
#--Trackbars for Changing HSV Range
cv2.createTrackbar('H Lower', 'HSV Range', 0, 255, nothing)
cv2.createTrackbar('S Lower', 'HSV Range', 0, 255, nothing)
cv2.createTrackbar('V Lower', 'HSV Range', 0, 255, nothing)
cv2.createTrackbar('H Upper', 'HSV Range', 0, 255, nothing)
cv2.createTrackbar('S Upper', 'HSV Range', 0, 255, nothing)
cv2.createTrackbar('V Upper', 'HSV Range', 0, 255, nothing)
#--Strting the Program
while True:
# Save each frame of video and store it in fram.
# Then convert frame BGR color type to HSV
# Note: We need to use '_,' behind of frame because we are convert it to HSV
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Define lower and upper element as a numpy array
lower_element = np.array([0, 0, 0])
upper_element = np.array([0, 0, 0])
#-----------------Read down blew text
"""
Track The Current Position of All Trackbars
and Switch.
"""
r = cv2.getTrackbarPos('R', 'HSV BGR')
g = cv2.getTrackbarPos('G', 'HSV BGR')
b = cv2.getTrackbarPos('B', 'HSV BGR')
h = cv2.getTrackbarPos('H', 'HSV BGR')
s = cv2.getTrackbarPos('S', 'HSV BGR')
