def testLut():
testimg = cv2.imread('/Users/zjj/Desktop/IMG_3271.jpg')
lutpath = '/Users/zjj/Desktop/lookup_0_origin_2.png'
lut = MYLUT(lutpath)
img = testimg.copy()
img = lut.imageInLut(img)
cv2.imwrite('/Users/zjj/Desktop/IMG_3271_lut.jpg', img)
return 0
# testimg = cv2.imread('/Users/zjj/Downloads/照片/IMG_0495.JPG')\
# testimg = cv2.resize(testimg, (600, 400))
fileinlutdir = os.listdir('lut')
fileinlutdir = sorted(fileinlutdir)
for filename in fileinlutdir:
if filename.endswith('.png') and filename.startswith('lookup'):
lutpath = 'lut/' + filename
print('Init', time.time())
lut = MYLUT(lutpath)
img = testimg.copy()
for i in range(1):
print('Start', time.time())
img = lut.imageInLut(img)
print('Finish', time.time())
windowname = filename + str(i)
cv2.namedWindow(windowname, cv2.WINDOW_KEEPRATIO)
cv2.resizeWindow(windowname, 1080, 1920)
cv2.imshow(windowname, img)
cv2.waitKey(0)
cv2.destroyWindow(windowname)
print("Done")
def detectColor():
def empty(a):
pass
cv2.namedWindow("TrackBars")
cv2.resizeWindow("TrackBars", 640, 240)
cv2.createTrackbar("Hue Min", "TrackBars", 0, 179, empty)
cv2.createTrackbar("Sat Min", "TrackBars", 0, 255, empty)
cv2.createTrackbar("Val Min", "TrackBars", 0, 255, empty)
cv2.createTrackbar("Hue Max", "TrackBars", 179, 179, empty)
cv2.createTrackbar("Sat Max", "TrackBars", 255, 255, empty)
cv2.createTrackbar("Val Max", "TrackBars", 255, 255, empty)
while True:
img = cv2.imread("./lambo.jpeg")
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h_min = cv2.getTrackbarPos("Hue Min", "TrackBars")
h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
#print(v_min)
# lower1 = np.array([h_min, s_min, v_min])
# upper1 = np.array([h_max, s_max, v_max])
lower = np.array([000, 000, 186])
upper = np.array([179, 255, 255])
mask = cv2.inRange(imgHSV, lower, upper)
imgResult = cv2.bitwise_and(img, img, mask=mask)
cv2.imshow("Original", img)
cv2.imshow("HSV", imgHSV)
cv2.imshow("Mask", mask)
cv2.imshow("Result", imgResult)
cv2.waitKey(0)
def displayIMG(img, windowName):
cv2.namedWindow( windowName, cv2.WINDOW_NORMAL )
cv2.resizeWindow(windowName, 1000, 800)
cv2.imshow(windowName, img)
def __init__(self, num, color=None):
if color == 'white': # predefined values for mapping whites only
init_h_min, init_h_max, init_s_min, init_s_max, init_v_min, init_v_max = 0, 179, 0, 59, 220, 255
init_thr1, init_thr2, init_area = 166, 171, 2000
elif color == 'green':
init_h_min, init_h_max, init_s_min, init_s_max, init_v_min, init_v_max = 55, 90, 30, 170, 40, 245
init_thr1, init_thr2, init_area = 0, 255, 3000
else:
init_h_min, init_h_max, init_s_min, init_s_max, init_v_min, init_v_max = 0, 179, 0, 255, 0, 255
init_thr1, init_thr2, init_area = 0, 255, 0
cv2.namedWindow(f"HSV_{num}")
cv2.resizeWindow(f"HSV_{num}", 640, 240)
cv2.createTrackbar(f"HUE Min_{num}", f"HSV_{num}", init_h_min, 179,
empty)
cv2.createTrackbar(f"HUE Max_{num}", f"HSV_{num}", init_h_max, 179,
empty)
cv2.createTrackbar(f"SAT Min_{num}", f"HSV_{num}", init_s_min, 255,
empty)
cv2.createTrackbar(f"SAT Max_{num}", f"HSV_{num}", init_s_max, 255,
empty)
cv2.createTrackbar(f"VALUE Min_{num}", f"HSV_{num}", init_v_min, 255,
empty)
cv2.createTrackbar(f"VALUE Max_{num}", f"HSV_{num}", init_v_max, 255,
empty)
cv2.namedWindow(f"Parameters_{num}")
cv2.resizeWindow(f"Parameters_{num}", 640, 240)
cv2.createTrackbar(f"Threshold1_{num}", f"Parameters_{num}", init_thr1,
255, empty)
cv2.createTrackbar(f"Threshold2_{num}", f"Parameters_{num}", init_thr2,
255, empty)
cv2.createTrackbar(f"Area_{num}", f"Parameters_{num}", init_area,
30000, empty)
self.num = num
def initialize_trackbars(initial_trackbar_values):
cv.namedWindow("Adaptive Threshold")
cv.resizeWindow("Adaptive Threshold", 360, 240)
cv.createTrackbar('thresh_val', 'Adaptive Threshold', initial_trackbar_values[0], 1, do_nothing)
cv.createTrackbar('adapt_val', 'Adaptive Threshold', initial_trackbar_values[1], 1, do_nothing)
cv.createTrackbar("high_threshold", 'Adaptive Threshold', initial_trackbar_values[2], 10, do_nothing)
cv.createTrackbar("block_size", 'Adaptive Threshold', initial_trackbar_values[3], 101, do_nothing)
cv.createTrackbar("sub_const", 'Adaptive Threshold', initial_trackbar_values[4], 100, do_nothing)
def init(row, col, win_name):
# win_name = str(x) + str(y)
if win_name in windowses:
return
cv.namedWindow(win_name, cv.WINDOW_NORMAL)
cv.resizeWindow(win_name, WIN_SIZE[0], WIN_SIZE[1])
cv.moveWindow(win_name, col * WIN_SIZE[0], row * WIN_SIZE[1])
windowses.add(win_name)
def initialize_trackbars(initial_trackbar_values):
cv.namedWindow("TrackBars")
cv.resizeWindow("TrackBars", 360, 240)
cv.createTrackbar('min', 'TrackBars', initial_trackbar_values[0], 255,
do_nothing)
cv.createTrackbar('max', 'TrackBars', initial_trackbar_values[1], 255,
do_nothing)
cv.createTrackbar("apertureSize", 'TrackBars', initial_trackbar_values[2],
7, do_nothing)
def btn2State(self):
global img
if self.btn_2.isEnabled():
self.btn_3.setEnabled(True)
# print("Btn_2被点击")
get_window_jpg()
cv2.resizeWindow('image', 640, 480)
# get_image_roi(img_cv)
lab_data()
def adjust_circle(image, circle):
"""Manually adjust a circle on an image. Takes an input image and
circle(center, radius) and shows a blown up region centered around the
circle. Allows the user to adjust the circle using trackbars. Waits
for keypress to finish. Returns adjusted circle and keypress."""
# initialize window and trackbars
win = 'Adjust Target Circle'
cv2.namedWindow(win)
cv2.resizeWindow(win, 200, 200)
# initialize variables
roi, roi_origin = get_circle_roi(image, circle)
circle_local = np.copy(circle)
circle_local[0] = circle[0] - np.flipud(roi_origin)
# scale image to be bigger and allow for easier adjustment
scale = cf['roi']['ADJUST_SCALE']
roi = scale_image(roi, scale)
circle_local = np.multiply(circle_local, scale)
img_circ = np.copy(roi)
# Set max radius of circle such that the max diameter is the length
# of the region of interest
max_radius = roi.shape[0] // 2
# initialize trackbars
cv2.createTrackbar('x', win,
circle_local[0][0], roi.shape[1], empty_callback)
cv2.createTrackbar('y', win,
circle_local[0][1], roi.shape[0], empty_callback)
cv2.createTrackbar('r', win,
circle_local[1], max_radius, empty_callback)
keypress = -1
while keypress == -1:
cv2.circle(img_circ,
(cv2.getTrackbarPos('x', win),
cv2.getTrackbarPos('y', win)),
cv2.getTrackbarPos('r', win),
(0, 0, 0),
1)
cv2.imshow(win, img_circ)
img_circ = np.copy(roi)
keypress = cv2.waitKey(5)
adj_circle = ((cv2.getTrackbarPos('x', win) // scale +
roi_origin[1],
cv2.getTrackbarPos('y', win) // scale +
roi_origin[0]),
cv2.getTrackbarPos('r', win) // scale)
cv2.destroyWindow(win)
return adj_circle, keypress
def show_img(img):
"""
If there is a matched image, the function shows it on screen
:param img: image to show on screen
"""
cv2.namedWindow("Painting rectification", cv2.WINDOW_KEEPRATIO)
cv2.imshow("Painting rectification", img)
cv2.resizeWindow("Painting rectification", int(img.shape[1] / 2),
int(img.shape[0] / 2))
cv2.waitKey(0)
def showPolygon(self, **kw):
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('image', 1001, 1001) #定义frame的大小
cv2.imshow('image', self.img)
if len(kw) > 0:
cv2.imwrite(
'/Users/sean/Documents/Projects/Algorithm Learn/Packing Algorithm/image/'
+ kw["_type"] + time.asctime(time.localtime(time.time())) +
'.png', self.img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def __init__(self):
cv2.namedWindow("trackBars")
cv2.resizeWindow("trackBars", 640, 240)
def empty(a):
pass
cv2.createTrackbar("Hue min", "trackBars", 0, 179, empty)
cv2.createTrackbar("Hue max", "trackBars", 179, 179, empty)
cv2.createTrackbar("Sat min", "trackBars", 50, 255, empty)
cv2.createTrackbar("Sat max", "trackBars", 255, 255, empty)
cv2.createTrackbar("Value min", "trackBars", 0, 255, empty)
cv2.createTrackbar("Value max", "trackBars", 255, 255, empty)
def trackbarwindow(self):
def empty(a):
pass
cv2.namedWindow(self.winname)
cv2.resizeWindow(self.winname, 640, 240)
cv2.createTrackbar(self.trackbar_name[0], self.winname, 0, 179, empty)
cv2.createTrackbar(self.trackbar_name[1], self.winname, 88, 179, empty)
cv2.createTrackbar(self.trackbar_name[2], self.winname, 41, 255, empty)
cv2.createTrackbar(self.trackbar_name[3], self.winname, 255, 255,
empty)
cv2.createTrackbar(self.trackbar_name[4], self.winname, 114, 255,
empty)
cv2.createTrackbar(self.trackbar_name[5], self.winname, 255, 255,
empty)
def findColor(
): #Finds the color of an object by user varying slider to mask out object
def empty(a):
pass
frameWidth = 640
frameHeight = 480
cap = cv2.VideoCapture(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10, 150)
cv2.namedWindow("HSV")
cv2.resizeWindow("HSV", 640, 240)
cv2.createTrackbar("Hue Min", "HSV", 0, 179, empty)
cv2.createTrackbar("Sat Min", "HSV", 0, 255, empty)
cv2.createTrackbar("Val Min", "HSV", 0, 255, empty)
cv2.createTrackbar("Hue Max", "HSV", 179, 179, empty)
cv2.createTrackbar("Sat Max", "HSV", 255, 255, empty)
cv2.createTrackbar("Val Max", "HSV", 255, 255, empty)
while True:
_, img = cap.read()
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h_min = cv2.getTrackbarPos("Hue Min", "HSV")
h_max = cv2.getTrackbarPos("Hue Max", "HSV")
s_min = cv2.getTrackbarPos("Sat Min", "HSV")
s_max = cv2.getTrackbarPos("Sat Max", "HSV")
v_min = cv2.getTrackbarPos("Val Min", "HSV")
v_max = cv2.getTrackbarPos("Val Max", "HSV")
#print(v_min)
lower = np.array([h_min, s_min, v_min])
upper = np.array([h_max, s_max, v_max])
mask = cv2.inRange(imgHSV, lower, upper)
result = cv2.bitwise_and(img, img, mask=mask)
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
cv2.imshow("Original", img)
cv2.imshow("Mask", mask)
cv2.imshow("Result", result)
if cv2.waitKey(1) & 11111111 == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def calc_matrix(reference_points_target):
global img
cam = VideoCapture(0) # 0 -> index of camera
s, img = cam.read()
cv2.namedWindow("image", cv2.WINDOW_NORMAL)
cv2.resizeWindow("image", 1280, 720)
cv2.setMouseCallback("image", draw_circle)
while (1):
cv2.imshow('image', img)
k = cv2.waitKey(20) & 0xFF
if k == 27:
break
elif k == ord("p"):
print(mouseX, mouseY)
elif k == ord("b"):
pos_blue = [mouseX, mouseY]
print('Blue Marked at ' + str(pos_blue))
elif k == ord("r"):
pos_red = [mouseX, mouseY]
print('Red Marked at ' + str(pos_red))
elif k == ord("y"):
pos_yellow = [mouseX, mouseY]
print('Yellow Marked at ' + str(pos_yellow))
elif k == ord("q"):
cv2.destroyAllWindows()
break
try:
pos_blue
pos_red
pos_yellow
except NameError:
raise Exception("Not all three reference points were assigned")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # set CV2 color scale to RGB
rows, cols, ch = img.shape
pts1 = np.float32([pos_blue, pos_red, pos_yellow])
pts2 = np.float32(reference_points_target)
M = cv2.getAffineTransform(pts1, pts2)
return M
def takeVal():
img = cv2.imread('SysImages/Parameters.png')[:160, :, :]
img = cv2.resize(img, (340, 120))
cv2.namedWindow('Parameters')
cv2.imshow('Parameters', img)
cv2.resizeWindow('Parameters', 640, 240)
cv2.createTrackbar('HUE Min', 'Parameters', 150, 255, empty)
cv2.createTrackbar('HUE Max', 'Parameters', 190, 255, empty)
cv2.createTrackbar('SAT Min', 'Parameters', 195, 255, empty)
cv2.createTrackbar('SAT Max', 'Parameters', 255, 255, empty)
cv2.createTrackbar('VALUE Min', 'Parameters', 195, 255, empty)
cv2.createTrackbar('VALUE Max', 'Parameters', 255, 255, empty)
cv2.createTrackbar('Thresh1', 'Parameters', 0, 255, empty)
cv2.createTrackbar('Thresh2', 'Parameters', 225, 255, empty)
cv2.createTrackbar('Area Min', 'Parameters', 100, 1000, empty)
def detect_color(path):
# add track bar
def empty(x):
pass
cv2.namedWindow('TrackBar')
cv2.resizeWindow('TrackBar', 640, 240)
cv2.createTrackbar('Hue min', 'TrackBar', 0, 179, empty)
cv2.createTrackbar('Hue max', 'TrackBar', 179, 179, empty)
cv2.createTrackbar('Saturation min', 'TrackBar', 0, 255, empty)
cv2.createTrackbar('Saturation max', 'TrackBar', 255, 255, empty)
cv2.createTrackbar('Val min', 'TrackBar', 0, 255, empty)
cv2.createTrackbar('Val max', 'TrackBar', 255, 255, empty)
while True:
img = cv2.imread(path)
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# get the values from track bar
h_min = cv2.getTrackbarPos('Hue min', 'TrackBar')
h_max = cv2.getTrackbarPos('Hue max', 'TrackBar')
s_min = cv2.getTrackbarPos('Saturation min', 'TrackBar')
s_max = cv2.getTrackbarPos('Saturation max', 'TrackBar')
v_min = cv2.getTrackbarPos('Val min', 'TrackBar')
v_max = cv2.getTrackbarPos('Val max', 'TrackBar')
lower = np.array([h_min, s_min, v_min])
upper = np.array([h_max, s_max, v_max])
mask = cv2.inRange(imgHSV, lower, upper)
imgMasked = cv2.bitwise_and(img, img, mask=mask)
# cv2.imshow('Original image', img)
# cv2.imshow('HSV image', imgHSV)
# cv2.imshow('Mask image', mask)
# cv2.imshow('Masked image', imgMasked)
imgStack = stackImages(0.6, ([img, imgHSV], [mask, imgMasked]))
cv2.imshow('Stacked Images', imgStack)
cv2.waitKey(1)
def get_image_roi(rgb_image):
'''
获得用户ROI区域的rect=[x,y,w,h]
:param rgb_image:
:return:
'''
# bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
global g_rect
img = rgb_image
cv2.namedWindow('image')
cv2.resizeWindow('image', 640, 480)
while True:
cv2.setMouseCallback('image', on_mouse)
cv2.resizeWindow('image', 640, 480)
cv2.startWindowThread() # 加在这个位置
cv2.imshow('image', img)
key = cv2.waitKey(0)
if cv2.getWindowProperty('image', cv2.WND_PROP_AUTOSIZE) < 1:
break
cv2.destroyAllWindows()
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return g_rect
def webCam ():
url = "http://172.28.131.139:8080/shot.jpg"
cv2.namedWindow("ipcam",cv2.WINDOW_NORMAL)
cv2.resizeWindow("ipcam",800,800)
while True:
imgRes=urllib.request.urlopen(url)
imgArray= np.array(bytearray(imgRes.read()),dtype=np.uint8)
img =cv2.imdecode(imgArray,-1)
cv2.imshow("ipcam",img)
img3=np.copy(img)
img4=np.copy(img)
LINES=Thread(target= houghline_transform, args=(img,))
LINES.start()
#ret=LINES.join()
ret=result.get()
hsv = cv2.cvtColor(img3, cv2.COLOR_BGR2HSV)
Gray_scaled_image=Gray_image(hsv)
gray_copy=np.copy(Gray_scaled_image)
filtered_image2=region_of_interest(img4,gray_copy)
#print(ret)
#LINES=houghline_transform(img)
img2=np.copy(img)
#average_slope_intercept(img2,LINES)
#_thread.start_new_thread( loop, (ret,gray_copy,img4,filtered_image2 ) )
for line in ret:
x1,y1,x2,y2 =line[0]
cv2.line(img4,(x1,y1),(x2,y2),(255,255,255),6) #draws a green line with a thickness equal to 3
cv2.line(gray_copy,(x1,y1),(x2,y2),(255,0,0),6)
cv2.line(filtered_image2,(x1,y1),(x2,y2),(255,0,0),6)
cv2.namedWindow("Gray_scale_with_lines",cv2.WINDOW_NORMAL)
cv2.resizeWindow("Gray_scale_with_lines",800,800)
cv2.imshow("Gray_scale_with_lines",gray_copy)
cv2.namedWindow("Image_with_lines",cv2.WINDOW_NORMAL)
cv2.resizeWindow("Image_scale_with_lines", 800, 800)
cv2.imshow("Image_with_lines",img4)
cv2.namedWindow("filtered",cv2.WINDOW_NORMAL)
cv2.resizeWindow("filtered", 800, 800)
cv2.imshow("filtered",filtered_image2)
_thread.start_new_thread( average_slope_intercept, (img2, ret, ) )
if cv2.waitKey(1)==27:
break
def showImage(image_lines, gray_lines, filter):
#with_objects=Countour_detection(img,Gray_scaled_image)
cv2.namedWindow("image_with_lines", cv2.WINDOW_NORMAL)
cv2.resizeWindow("image_with_lines", 200, 200)
cv2.imshow("image_with_lines", image_lines)
cv2.namedWindow("Filtered_image", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Filtered_image", 200, 200)
cv2.imshow("Filtered_image", filter)
cv2.namedWindow("Gray_scale_with_lines", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Gray_scale_with_lines", 200, 200)
cv2.imshow("Gray_scale_with_lines", gray_lines)
#cv2.imshow("with_objects",with_objects)
cv2.waitKey(1)
# Define all the global variables
heightImg = 640
widthImg = 480
Image_Resolution_height = 640
Image_Resolution_width = 480
# For Photo from live Webcam feed
#img_counter=utilities.webcamfeed(heightImg,widthImg,Image_Resolution_height,Image_Resolution_width)
img_counter = 2
#For Live Webcam feed, uncomment next line
#if webCamFeed:success, img = cap.read()
#else:
# Reading the Image
# Filepath of Image
pathImage = "opencv_frame_{}.png".format(img_counter - 1)
img = cv2.imread(pathImage)
heightImg, widthImg, _ = img.shape
# Trackbars for Threshold 1 and 2
cv2.namedWindow("Threshold Parameters")
cv2.resizeWindow("Threshold Parameters", 400, 600)
cv2.createTrackbar("Threshold 1", "Threshold Parameters", 60, 255,
utilities.nothing)
cv2.createTrackbar("Threshold 2", "Threshold Parameters", 60, 255,
utilities.nothing)
Warped_Img = utilities.process(img, heightImg, widthImg)
print(1)
utilities.adaptive_thresholding(Warped_Img, heightImg, widthImg)
from cv2 import cv2
import numpy as np
img = cv2.imread("Resources/lambo.png")
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
window = cv2.namedWindow("Trackbars")
cv2.resizeWindow("Trackbars", 512, 512)
cv2.createTrackbar("Hue Min", "Trackbars", 0, 179, lambda x: x)
cv2.createTrackbar("Hue Max", "Trackbars", 24, 179, lambda x: x)
cv2.createTrackbar("Sat Min", "Trackbars", 88, 255, lambda x: x)
cv2.createTrackbar("Sat Max", "Trackbars", 255, 255, lambda x: x)
cv2.createTrackbar("Val Min", "Trackbars", 137, 255, lambda x: x)
cv2.createTrackbar("Val Max", "Trackbars", 255, 255, lambda x: x)
while True:
mins = np.array([
cv2.getTrackbarPos("Hue Min", "Trackbars"),
cv2.getTrackbarPos("Sat Min", "Trackbars"),
cv2.getTrackbarPos("Val Min", "Trackbars")
],
dtype=np.uint8)
maxes = np.array([
cv2.getTrackbarPos("Hue Max", "Trackbars"),
cv2.getTrackbarPos("Sat Max", "Trackbars"),
cv2.getTrackbarPos("Val Max", "Trackbars")
],
dtype=np.uint8)
mask = cv2.inRange(imgHSV, mins, maxes)
result = cv2.bitwise_and(img, img, mask=mask)
hor[x] = np.hstack(imgArray[x])
ver = np.vstack(hor)
else:
for x in range(0, rows):
if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
else:
imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor= np.hstack(imgArray)
ver = hor
return ver
cv2.namedWindow("TrackBars") #craeting a trackbar to find optimal values of hue , sat and val
cv2.resizeWindow("TrackBars", 640,240)
cv2.createTrackbar("Hue Min", "TrackBars",0,179,empty)
cv2.createTrackbar("Hue Max", "TrackBars",179,179,empty)
cv2.createTrackbar("Sat Min", "TrackBars",0,255,empty)
cv2.createTrackbar("Sat Max", "TrackBars",255,255,empty)
cv2.createTrackbar("Val Min", "TrackBars",0,255,empty)
cv2.createTrackbar("Val Max", "TrackBars",255,255,empty)
livecap = cv2.VideoCapture(0)
livecap.set(3, 600) # 3 represents height here
livecap.set(4, 400) # 4 represents width here
livecap.set(10, 1000) # 10 represents brightnes
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale,
scale)
else:
imgArray[x] = cv2.resize(
imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]),
None, scale, scale)
if len(imgArray[x].shape) == 2:
imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor = np.hstack(imgArray)
ver = hor
return ver
# Define Trackbars
cv2.namedWindow('TrackBars')
cv2.resizeWindow('TrackBars', 640, 240)
cv2.createTrackbar('Hue Min', 'TrackBars', 0, 179, empty_fn)
cv2.createTrackbar('Hue Max', 'TrackBars', 19, 179, empty_fn)
cv2.createTrackbar('Sat Min', 'TrackBars', 110, 255, empty_fn)
cv2.createTrackbar('Sat Max', 'TrackBars', 240, 255, empty_fn)
cv2.createTrackbar('Val Min', 'TrackBars', 153, 255, empty_fn)
cv2.createTrackbar('Val Max', 'TrackBars', 255, 255, empty_fn)
while True:
# Read image file from path
path = 'Resources/lambo.png'
img = cv2.imread(path)
# convert image to HSV
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
def init_window(name, row=0, col=0):
cv.namedWindow(name, cv.WINDOW_NORMAL)
cv.resizeWindow(name, WIN_SIZE[0], WIN_SIZE[1])
cv.moveWindow(name, col * WIN_SIZE[0], row * WIN_SIZE[1])
warmup_img = np.zeros(shape=(IMG_SIZE[1], IMG_SIZE[0], 3), dtype=np.uint8)
show_image(warmup_img, name)
def Varredura():
#realiza a varredura e chama a função de analise de contornos
img = Camera()
#caso queira desabilitar a camera, é so comentar a linha de cima e usar as de baixo
#img = cv2.imread("<endereço da imagem>")
#img = cv2.resize(img, (639,479), interpolation = cv2.INTER_AREA)
#chama a função de enquadramento
img = Enquadramento(img)
#a função da janela declarada é impedir q a imagem apareça gigantesca e ñ perca qualidade
cv2.namedWindow('Deseja Recortar a imagem?', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Deseja Recortar a imagem?', (int(479 * proporcao), 479))
##### rotacionar 180º para melhor compreenção
altura, largura = img.shape[:2]
ponto = (largura / 2, altura / 2
) #ponto no centro da figura, ponto de giro
rotacao = cv2.getRotationMatrix2D(ponto, 180, 1.0)
img = cv2.warpAffine(img, rotacao, (largura, altura))
###primeira janela pra corte
print("\n\n\n")
print("--Deseja recortar a imagem?--")
print(
"-Sim:\n Selecione clicando e arrastando.\n Confirma com a tecla Enter"
)
print("-Não:\n Aperte a tecla Esc\n\n\n")
xs, ys, w, h = cv2.selectROI(
'Deseja Recortar a imagem?',
img) #permite o usuario cortar a imagem com o mouse
cv2.destroyAllWindows()
crop_img_true = crop_img_contour = img[ys:ys + h, xs:xs +
w] #salvandoos dados do corte
cortada = True
if crop_img_true.shape[0] < 1:
cortada = False
crop_img_true = crop_img_contour = img
print("\n\n\n")
print(
"--Utilize as barras para evidenciar, da melhor forma possível, os objetos desejados--"
)
print(
"--O filtro dinâmico possui limitações, logo, talvez não seja possível evidenciar todos os objetos desejados de um só vez--"
)
#filtro dinamico
desejo = "b"
while 1:
x, y, z, a, b, c = (
tr.tracker(crop_img_true)
) #biblioteca que gera filtro de cores de modo iterativo
crop_img_true = cv2.cvtColor(
crop_img_true, cv2.COLOR_BGR2HSV
) #é necessario converter para hsv para passar o filtro
#criando imagem base pra varredura com o contorno do filtro de cores
mask_inRange = cv2.inRange(crop_img_true, (x, y, z), (a, b, c))
#lógica para sobreposição de filtros, necessário caso o usuário queira evidencia cores distantes no espectro HSVe com intervalos
if desejo == "a":
mask_inRange = cv2.resize(mask_inRange,
(int(479 * proporcao), 479),
interpolation=cv2.INTER_AREA)
mask_inRange_temp = cv2.resize(mask_inRange_temp,
(int(479 * proporcao), 479),
interpolation=cv2.INTER_AREA)
for y in range(0, mask_inRange.shape[0]): #percorre linhas
for x in range(0, mask_inRange.shape[1]): #percorre colunas
#mask eh uma imagem composta apenas de 0 e 255
if (mask_inRange_temp[y][x] == (255)):
mask_inRange[y][x] = (255)
mask_inRange = cv2.resize(mask_inRange,
(int(2500 * proporcao), 2500),
interpolation=cv2.INTER_AREA)
print("--Deseja avidenciar mais objetos?--")
print(" a - Sim \n b - Não")
desejo = input()
if desejo == "a":
mask_inRange_temp = mask_inRange
crop_img_true = cv2.cvtColor(crop_img_true, cv2.COLOR_HSV2BGR)
#caso o usuário envio qualquer coisa diferente de a, será considerado b
else:
break
#caso o usuário opte por recoertar a imagem, é necessario corrigir as medidas
if cortada == True:
blank_space_black = np.zeros((img.shape[0], img.shape[1]), np.uint8)
blank_space_black[:] = (0)
blank_space_black[ys:ys + h, xs:xs + w] = mask_inRange
mask_inRange = blank_space_black
crop_img_true = crop_img_contour = img
#etapa importante para que a função findContours funcione sem problemas
_, threshold = cv2.threshold(mask_inRange, 250, 255, cv2.THRESH_BINARY)
#varredura de contornos
contours = []
contours, _ = cv2.findContours(threshold, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)
#listas de bordas
contornos_reais = []
total_objetos = 0
# tirando objetos menores que o minimo... retira ruido
for cnt in contours:
if cv2.contourArea(cnt) >= maxi:
contornos_reais.append(cnt)
total_objetos += 1
print("\n\n")
print("-- Total de objetos encontrados", total_objetos, " --")
# todos os dados são obtidos atraves da mascara, uma imagem praticamente binária.
#as edições (contornos, escrever o ID no centro do objeto) são feitas em cima da imagem crop_img_contour que é a mesma imagem justificada, enquadrada lá do inicio
#desenhar contornos
cv2.drawContours(crop_img_contour, contornos_reais, -1, (0, 255, 0), 5)
crop_img_contour, tabela = Identificar_objeto(
contornos_reais, crop_img_contour,
img) #chamar a função Identificar_objetos
img_final = cv2.resize(crop_img_contour, (850, 512),
interpolation=cv2.INTER_AREA)
cv2.imwrite("img_final.jpg",
img_final) #salvando a imagem na pasta do programa
###tabela
tabela_df = pd.DataFrame(data=tabela,
columns=[
'Id',
'Centro_X',
'Centro_Y',
'Ângulo',
])
tabela_df.to_csv("tabela.csv")
return tabela_df, img_final
# Ref: https://github.com/PyUserInput/PyUserInput
# Usage:
# m.click(0, 0, 1) 使用 X11 坐标
# k.type_string('Hello, World!')
m = PyMouse()
k = PyKeyboard()
# Screen detection
print('[加载]屏幕参数')
x11_x, x11_y = m.screen_size()
pil_x, pil_y = ImageGrab.grab().size
DPI_times = pil_x / x11_x
EMULATOR_HEADER = 39
EMULATOR_BOTTOM = 42
cv2.namedWindow('output', cv2.WINDOW_NORMAL)
cv2.resizeWindow('output', 500, 500)
cv2.moveWindow('output', int(x11_x) - 500, int(x11_y) - 500)
print('[加载]X11 size:', (x11_x, x11_y))
print('[加载]PIL size:', ImageGrab.grab().size)
print('[加载]DPI times:', DPI_times)
# Helpers - 图像处理
screen_data = None
class ScanIter(object):
def __init__(self, startp, endp):
self.start_x, self.start_y = startp
self.end_x, self.end_y = endp
def __iter__(self):
u, v = img.shape[:2]
# 找到棋盘格角点
ret, corners = cv2.findChessboardCorners(gray, (w,h),None)
# 如果找到足够点对，将其存储起来
if ret == True:
print("i:", i)
i = i+1
# 在原角点的基础上寻找亚像素角点
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
#追加进入世界三维点和平面二维点中
objpoints.append(objp)
imgpoints.append(corners)
# 将角点在图像上显示
cv2.drawChessboardCorners(img, (w,h), corners, ret)
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.resizeWindow('findCorners', 640, 480)
cv2.imshow('findCorners',img)
cv2.waitKey(200)
cv2.destroyAllWindows()
#%% 标定
print('正在计算')
#标定
ret, mtx, dist, rvecs, tvecs = \
cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("ret:",ret )
print("mtx:\n",mtx) # 内参数矩阵
print("dist畸变值:\n",dist ) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
print("rvecs旋转（向量）外参:\n",rvecs) # 旋转向量 # 外参数
print("tvecs平移（向量）外参:\n",tvecs ) # 平移向量 # 外参数
# python object_size.py --image images/example_03.png --width 3.5
# python object_size.py --image images/test_01.jpg --width 1
# python object_size.py --image images/test_02.jpg --width 1
# python object_size.py --image images/test_04.jpg --width 0.787402
# import the necessary packages
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
from cv2 import cv2
import numpy as np
import argparse
import imutils
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('image', 1920, 1080)
def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to the input image")
ap.add_argument("-w", "--width", type=float, required=True,
help="width of the left-most object in the image (in inches)")
args = vars(ap.parse_args())
# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread(args["image"])
cv2.imshow("image", image)
import math
from sympy import Point, Line, Circle, intersection, Ray, pi
from cv2 import cv2 as cv
from sympy import plot_implicit, cos, sin, symbols, Eq, And
from sympy import symbols
from sympy.plotting import plot
import matplotlib.pyplot as plt
from numba import jit, cuda
def nothing(x):
pass
cv.namedWindow('Image', cv.WINDOW_NORMAL)
cv.resizeWindow('Image', 600, 600)
def during_collision(cue_point, radius, stick_point, ball_coord):
future_point = cue_point
collision_ball_info = cue_point
min_distance = 1e9
temp_ray = Ray(cue_point, stick_point)
temp_Line = Line(cue_point, stick_point)
temp_circle = Circle(cue_point, radius)
temp_collision_points = intersection(temp_Line, temp_circle)
if temp_ray.contains(temp_collision_points[0]):
white_ray = Ray(cue_point, temp_collision_points[1])
else:
