def drawQuarterCircle(img,
color,
center=None,
axes=(40, 40),
angle=0,
sAngle=None,
eAngle=None,
thic=-1):
dir = random.randint(1, 4)
if center == None:
#Quadrant 1
if dir == 1:
center = (5, 44)
sAngle = 270
eAngle = 360
#Quadrant 2
elif dir == 2:
center = (44, 44)
sAngle = 180
eAngle = 270
#Quadrant 3
elif dir == 3:
center = (44, 5)
sAngle = 90
eAngle = 180
#Quadrant 4
else:
center = (5, 5)
sAngle = 0
eAngle = 90
cv2.ellipse(img, center, axes, angle, sAngle, eAngle, color, thic)
def create_emoticon():
emoticon = np.zeros([512, 512, 3], np.uint8)
emoticon = cv2.circle(emoticon, (256, 256), 200, (0, 215, 255), -1)
emoticon = cv2.ellipse(emoticon, (166, 200), (40, 30), 90, 0, 360,
(0, 0, 0), -1)
emoticon = cv2.ellipse(emoticon, (166, 200), (40, 30), 90, 0, 360,
(255, 255, 255), 1)
emoticon = cv2.ellipse(emoticon, (346, 200), (40, 30), 90, 0, 360,
(0, 0, 0), -1)
emoticon = cv2.ellipse(emoticon, (346, 200), (40, 30), 90, 0, 360,
(255, 255, 255), 1)
emoticon = cv2.circle(emoticon, (180, 175), 7, (255, 255, 255), -1)
emoticon = cv2.circle(emoticon, (360, 175), 7, (255, 255, 255), -1)
pts = [(130, 260), (150, 390), (362, 390), (382, 260), (256, 248)]
cv2.fillPoly(emoticon, np.array([pts]), (208, 224, 64))
cv2.polylines(emoticon, np.array([pts]), True, (255, 255, 255), 2)
cv2.line(emoticon, (150, 290), (362, 290), (209, 206, 0), 2)
cv2.line(emoticon, (155, 330), (357, 330), (209, 206, 0), 2)
cv2.line(emoticon, (160, 360), (352, 360), (209, 206, 0), 2)
cv2.line(emoticon, (130, 260), (65, 200), (255, 255, 255), 2)
cv2.line(emoticon, (382, 260), (447, 200), (255, 255, 255), 2)
cv2.line(emoticon, (150, 390), (125, 405), (255, 255, 255), 2)
cv2.line(emoticon, (362, 390), (387, 405), (255, 255, 255), 2)
cv2.imshow('emoticon', emoticon)
cv2.waitKey(0)
cv2.destroyAllWindows()
def describe(self, image):
# convert the image to the HSV color space and initialize
# the features used to quantify the image
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
features = []
# grab the dimensions and compute the center of the image
(h, w) = image.shape[:2]
(cX, cY) = (int(w * 0.5), int(h * 0.5))
# divide the image into four rectangles/segments (top-left,
# top-right, bottom-right, bottom-left)
segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h),
(0, cX, cY, h)]
# construct an elliptical mask representing the center of the
# image
(axesX, axesY) = (int(w * 0.75) // 2, int(h * 0.75) // 2)
ellipMask = np.zeros(image.shape[:2], dtype="uint8")
cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1)
# loop over the segments
for (startX, endX, startY, endY) in segments:
# construct a mask for each corner of the image, subtracting
# the elliptical center from it
cornerMask = np.zeros(image.shape[:2], dtype="uint8")
cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)
cornerMask = cv2.subtract(cornerMask, ellipMask)
# extract a color histogram from the image, then update the
# feature vector
hist = self.histogram(image, cornerMask)
features.extend(hist)
# extract a color histogram from the elliptical region and
# update the feature vector
hist = self.histogram(image, ellipMask)
features.extend(hist)
# return the feature vector
return features
def detectAndDisplay(frame):
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame_gray = cv2.equalizeHist(frame_gray)
# Detectar faces
# Divide a imagem em diversos retângulos e retorna uma lista com eles
faces = face_cascade.detectMultiScale(frame_gray)
for (x, y, w, h) in faces:
center = (x + w // 2, y + h // 2)
# Elipses rosas
frame = cv2.ellipse(frame, center, (w // 2, h // 2), 0, 0, 360,
(255, 0, 255), 4)
faceROI = frame_gray[y:y + h, x:x + w]
# Em cada face, detectar olhos
eyes = eyes_cascade.detectMultiScale(faceROI)
for (x2, y2, w2, h2) in eyes:
eye_center = (x + x2 + w2 // 2, y + y2 + h2 // 2)
radius = int(round((w2 + h2) * 0.25))
# Circulos azuis
frame = cv2.circle(frame, eye_center, radius, (255, 0, 0), 4)
# Frame invertido horizontalmente por questão de estética =)
frame = cv2.flip(frame, 1)
cv2.imshow('Deteccao de Face e Olho', frame)
return frame
def choose_target_point(skel):
""" Selects a target poitn from skeleton for pure pursuit.
Draws a ellipse and applies an and operation to the ellipse with the skel.
Then returns a point that has least distance with the center of the
ellipse.
Args:
skel: skeleton of trajectory.
Returns:
target_point: target point for pure pursuit.
"""
width = skel.shape[1]
height = skel.shape[0]
img = np.zeros((height, width), dtype=np.uint8)
ellipse = cv.ellipse(img,
center=(width // 2, height),
axes=(width // 3, height // 2),
angle=0,
startAngle=180,
endAngle=360,
color=255,
thickness=1)
img_points = np.bitwise_and(skel, ellipse)
_, contours, _ = cv.findContours(img_points,
mode=cv.RETR_EXTERNAL,
method=cv.CHAIN_APPROX_NONE)
discrete_points = []
img_DEBUG = np.zeros((height, width, 3), dtype=np.uint8)
img_DEBUG[:, :, 0] = skel
img_DEBUG[:, :, 1] = img_points
if DEBUG:
cv.imwrite("img_DEBUG.jpg", img_DEBUG)
# cv.waitKey(200)
for contour in contours:
if contour.size == 2:
discrete_points.append(np.squeeze(contour))
else:
pass
discrete_points = sorted(discrete_points,
key=lambda x: (x[0] - width // 2)**2 +
(x[1] - height)**2)
if len(discrete_points) != 0:
return discrete_points[0], width, height, img_DEBUG
else:
return [371, 0], width, height, img_DEBUG
def __draw_ellipse(self, image):
params = self.dict_parameters
keys = params.keys()
assert all(
key in keys for key in
['center', 'axes', 'angle', 'startAngle', 'endAngle', 'color'])
cv2.ellipse(image,
center=params['center'],
axes=params['axes'],
angle=params['angle'],
startAngle=params['startAngle'],
endAngle=params['endAngle'],
color=params['color'],
thickness=params.get('thickness') or 1,
lineType=params.get('lineType') or 8,
shift=params.get('shift') or 0)
def drawSemiCircle(img,
color,
center=None,
axes=(25, 25),
angle=0,
sAngle=0,
eAngle=None,
thic=-1):
dir = random.randint(1, 2)
if center == None:
if dir == 1:
center = (24, 14)
eAngle = 180
else:
center = (24, 35)
eAngle = -180
cv2.ellipse(img, center, axes, angle, sAngle, eAngle, color, thic)
def _update_agents_on_map(self, i, temp_pos_x, temp_pos_y):
color_b, color_g, color_r = self._agent_color_list[i]
self._gloabal_grid_with_agents = cv2.ellipse(self._global_grid_display,(temp_pos_x,temp_pos_y),\
(10,10),0,15,345,(color_b,color_g,color_r),-1)
self._known_grid_with_agents = cv2.ellipse(self._known_grid_display,(temp_pos_x,temp_pos_y),\
(10,10),0,15,345,(color_b,color_g,color_r),-1)
string_to_add = "Agent:" + str(i) + ", region:" + str(
self._region_col[i])
self._grid_with_regions_display = cv2.putText(
self._grid_with_regions_display, string_to_add,
(temp_pos_x, temp_pos_y + 25), self._font, 0.5, (0, 0, 0), 2,
cv2.LINE_AA)
self._grid_with_agents_and_regions = cv2.ellipse(
self._grid_with_regions_display, (temp_pos_x, temp_pos_y),
(10, 10), 0, 15, 345, (color_b, color_g, color_r), -1)
def show(image):
result = Recognition().recognize(image)
polygon = []
for qr in result['qr']:
pts = np.array(
list(
map(lambda point: [point.x, point.y],
qr['location'].points)), np.int32).reshape((-1, 1, 2))
polygon.append(pts)
for ring in result['rings']:
cv2.ellipse(img=image,
center=(int(ring.center.x), int(ring.center.y)),
axes=(int(ring.maxLength), int(ring.minLength)),
angle=ring.angle,
color=(0, 255, 255),
startAngle=0,
endAngle=360)
cv2.polylines(image, polygon, True, (0, 255, 255))
cv2.imshow('detected circles', image)
cv2.moveWindow('detected circles', 20, 20)
def load_mask(annos, datadir, imgid, imgdata, rois):
img = annos["imgs"][imgid]
mask = np.zeros(imgdata.shape[:-1])
mask_poly = np.zeros(imgdata.shape[:-1])
mask_ellipse = np.zeros(imgdata.shape[:-1])
for obj in img['objects']:
box = obj['bbox']
cv2.rectangle(mask, (int(box['xmin']), int(box['ymin'])), (int(box['xmax']), int(box['ymax'])), 1, -1)
if 'polygon' in obj and len(obj['polygon'])>0:
pts = np.array(obj['polygon'])
cv2.fillPoly(mask_poly, [pts.astype(np.int32)], 1)
rois.append(pts)
else:
cv2.rectangle(mask_poly, (int(box['xmin']), int(box['ymin'])), (int(box['xmax']), int(box['ymax'])), 1, -1)
if 'ellipse' in obj:
rbox = obj['ellipse']
rbox = ((rbox[0][0], rbox[0][1]), (rbox[1][0], rbox[1][1]), rbox[2])
rois.append(rbox)
cv2.ellipse(mask_ellipse, rbox, 1, -1)
else:
cv2.rectangle(mask_ellipse, (int(box['xmin']), int(box['ymin'])), (int(box['xmax']), int(box['ymax'])), 1, -1)
mask = np.multiply(np.multiply(mask,mask_poly),mask_ellipse)
return mask
def draw_circle(self, img, descirptor_in_use):
"""获取外接轮廓
:param res: 输入图像，傅里叶描述子
:return: 重绘图像
"""
contour_reconstruct = np.fft.ifft(descirptor_in_use) # 傅里叶反变换
contour_reconstruct = np.array(
[contour_reconstruct.real, contour_reconstruct.imag])
contour_reconstruct = np.transpose(contour_reconstruct) # 转换矩阵
contour_reconstruct = np.expand_dims(contour_reconstruct,
axis=1) # 改变数组维度在axis=1轴上加1
if contour_reconstruct.min() < 0:
contour_reconstruct -= contour_reconstruct.min()
contour_reconstruct *= img.shape[0] / contour_reconstruct.max()
contour_reconstruct = contour_reconstruct.astype(np.int32, copy=False)
x, y, w, h = cv2.boundingRect(contour_reconstruct) # 外接矩形
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 225, 0), 3)
rect = cv2.minAreaRect(contour_reconstruct) # 最小外接矩形
box = np.int0(cv2.boxPoints(rect)) # 矩形的四个角点取整
cv2.drawContours(img, [box], 0, (0, 255, 255), 3)
(x, y), radius = cv2.minEnclosingCircle(contour_reconstruct) # 最小外接圆
(x, y, radius) = np.int0((x, y, radius)) # 圆心和半径取整
cv2.circle(img, (x, y), radius, (0, 255, 0), 2)
ellipse = cv2.fitEllipse(contour_reconstruct) # 拟合椭圆
cv2.ellipse(img, ellipse, (0, 0, 255), 2)
df = pd.DataFrame(np.random.rand(10, 4),
columns=[u'外接矩形', u'最小外接矩阵', u'外接圆', u'椭圆'])
fig = df.plot(figsize=(6, 6)) # 创建图表对象，并复制给fig
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.xlabel(u'图像轮廓', fontsize=20)
plt.show()
return img
def detect_ransac(self,
img,
outlier_rate=0.4,
iterations=1000,
draw_ellipse=False):
'''
Detect valve center with RANSAC ellipse fitting algorithm.
@img:
np.array, input image.
@outlier_rate
float, rate of pre-estimated outliers in fitting points, should be larger than 0 and smaller than 1.0.
@iterations
int, positive int, which means the total iterations of RANSAC algorithm.
@return
(np.array, ellipse), image with fitted ellipse and ellipse parameters.
Parameters are exactly the same as the return values of OpenCV fitEllipse() function.
'''
edge = self._get_saturation_edge(img)
points = self._get_points(edge)
# traditional fitting
# elps = cv.fitEllipse(points)
# center, _, _ = elps
# x = int(center[0])
# y = int(center[1])
# img = cv.ellipse(img, elps, color=(0,255,0), thickness=1)
# img = cv.circle(img, (x, y), 2, (0,255,0), cv.FILLED)
# ransac
ellipse = self._fit_ellipse_ransac(points, outlier_rate, iterations)
center, params, _ = ellipse
x = int(center[0])
y = int(center[1])
if draw_ellipse:
img = cv.ellipse(img, ellipse, color=(0, 255, 255), thickness=1)
img = cv.circle(img, (x, y), 2, (0, 255, 255), cv.FILLED)
# print('ratio of axis(short/long):{0:.3f}'.format(params[0]/params[1]))
return img, ellipse
def place_agents_on_grid_with_info_of_assigned_region(self):
self.grid_with_region()
temp_grid = copy.copy(self._grid_with_regions)
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(self._no_of_agents):
temp_agent_pos = self._agenthandler.get_pos_of_agent(i)
a = np.array([temp_agent_pos['x'], temp_agent_pos['y']])
string_to_add = "Agent:" + str(i) + ", region:" + str(
self._col_ind[i])
temp_grid = cv2.putText(temp_grid, string_to_add,
(a[0], a[1] + 25), font, 0.5, (0, 0, 0), 2,
cv2.LINE_AA)
color_b, color_g, color_r = self._agent_color_list[i]
temp_grid = cv2.ellipse(temp_grid, (a[0], a[1]), (10, 10), 0, 15,
345, (color_b, color_g, color_r), -1)
self._grid_with_regions_and_agents = temp_grid
while cap.isOpened():
ret, frame = cap.read()
if ret:
t0 = time.time()
frame = cv2.flip(frame, 1)
cropped = frame[206 - 100:206 + 100, 256 - 100:256 + 100]
bboxes = detector.detect(cropped)
for box in bboxes:
x, y, width, height = box
x, y = x + 256 - 100, y + 206 - 100
x2, y2 = x + width, y + height
cv2.rectangle(frame, (x, y), (x2, y2), (0, 100, 100), 5)
cv2.ellipse(frame, (256, 206), (100, 100), 0, 0, 360, 50, 5)
cv2.rectangle(frame, (256 - 100, 206 - 100),
(256 + 100, 206 + 100), (0, 0, 255), 10)
t1 = time.time()
font = cv2.FONT_HERSHEY_PLAIN
org = (50, 50)
fontScale = 1
color = (255, 0, 0)
thickness = 2
message = f'{round(t1-t0, 3)} sec per frame'
cv2.putText(frame, message, org, font, fontScale, color, thickness,
cv2.LINE_AA)
cv2.imshow("preview", frame)
# out.write(frame)
# Rotated Rectangle is drawn with minimum area, so it considers the rotation also. The function used is cv2.minAreaRect(). It returns a Box2D structure which contains following detals - (top-left corner(x, y), (width, height), angle of rotation). But to draw this rectangle, we need 4 corners of the rectangle. It is obtained by the function cv2.boxPoints()
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
boundingRect = cv2.drawContours(img_rgb, [box], 0, (0, 0, 255), 2)
# cv2.minEnclosingCircle() find the circumcircle of an object. It is a circle which completely covers the object with minimum area.
(x, y), radius = cv2.minEnclosingCircle(cnt)
center = (int(x), int(y))
radius = int(radius)
circumcircle = cv2.circle(img_rgb2, center, radius, (0, 255, 0), 2)
# To fit an ellipse to an object, use the function cv2.fitEllipse(). It returns the rotated rectangle in which the ellipse is inscribed.
ellipse = cv2.fitEllipse(cnt)
fit = cv2.ellipse(img_rgb3, ellipse, (0, 255, 0), 2)
# Similarly we can fit a line to a set of points. Below image contains a set of white points. We can approximate a straight line to it.
rows, cols = img.shape[:2]
[vx, vy, x, y] = cv2.fitLine(cnt, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = int((-x*vy/vx) + y)
righty = int(((cols-x)*vy/vx)+y)
fit = cv2.line(img_rgb3, (cols-1, righty), (0, lefty), (0, 255, 0), 2)
plt.subplot(2, 2, 1), plt.imshow(img, cmap='gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 2), plt.imshow(boundingRect, cmap='gray')
plt.title('boundingRect and RotatedRect'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 3), plt.imshow(circumcircle, cmap='gray')
plt.title('minEnclosingCircle'), plt.xticks([]), plt.yticks([])
cv2.putText(frame, "Center roiBox", (0, 10), font, 0.5, (0, 255, 0), 2,
cv2.LINE_AA)
cv2.putText(frame, "Estimated Pos", (0, 30), font, 0.5, (255, 255, 0),
2, cv2.LINE_AA)
cv2.putText(frame, "Prediction", (0, 50), font, 0.5, (0, 0, 255), 2,
cv2.LINE_AA)
rgbr = np.floor_divide(colorMask, REDU)
r, g, b = rgbr.transpose(2, 0, 1)
l = his[r, g, b]
maxl = l.max()
aa = np.clip((l * l / maxl * 255), 0, 255).astype(np.uint8)
(rb, roiBox) = cv2.CamShift(l, roiBox, termination)
cv2.ellipse(frame, rb, (0, 255, 0), 2)
xo = int(roiBox[0] + roiBox[2] / 2)
yo = int(roiBox[1] + roiBox[3] / 2)
error = (roiBox[3])
if (yo < error or fgmask.sum() < 50):
mu, P, pred = kalman(mu, P, F, Q, B, a, None, H, R)
m = "None"
mm = False
else:
mu, P, pred = kalman(mu, P, F, Q, B, a, np.array([xo, yo]), H, R)
m = "normal"
mm = True
if (mm):
def convex(ImgNo, defThr=127):
img = cv2.imread(impDef.select_img(ImgNo))
img2 = img.copy()
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thr = cv2.threshold(imgray, defThr, 255, 0)
contours, _ = cv2.findContours(thr, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cntI = 80
# for i in contours:
# cnt0 = contours[cntI]
# cv2.drawContours(img, [cnt0], 0, (0, 0, 255), 2)
# cv2.imshow('contour', img)
# print('cntI = ', cntI)
# impDef.close_window( )
# cntI = cntI+1
cnt = contours[88]
#cv2.moments() 함수를 이용해 한반도의 무게 중심 좌표를 구합니다
mmt = cv2.moments(cnt)
cx = int(mmt['m10'] / mmt['m00'])
cy = int(mmt['m01'] / mmt['m00'])
x, y, w, h = cv2.boundingRect(cnt)
korea_rect_area = w * h
korea_area = cv2.contourArea(cnt)
hull = cv2.convexHull(cnt)
hull_area = cv2.contourArea(hull)
ellipse = cv2.fitEllipse(cnt)
aspect_ratio = w / h
extent = korea_area / korea_rect_area
solidity = korea_area / hull_area
print('대한민국 Aspect Ratio : \t%.3f' % aspect_ratio)
print('대한미국 Extent : \t%.3f' % extent)
print('대한민국 Solidity : \t%.3f' % solidity)
print('대한민국 Orientation : \t%.3f' % ellipse[2])
# Orientation of Korea는 한반도 Contour에 최적 타원의 방향입니다.
# 이 값은 cv2.fitEllipse() 함수의 리턴값인 ellipse의 3번째 멤버인 ellipse[2] 값이며,
# 수직방향을 기준으로 타원이 회전한 각도를 나타냅니다.
equivalent_diameter = np.sqrt(4 * korea_area / np.pi)
korea_radius = int(equivalent_diameter / 2)
# 한반도 무게 중심을 빨간색 원으로 표시를 합니다.
# 그리고 이 무게중심을 중심으로 한 한반도와 면적이 동일한 빨간색 원을 그렸습니다.
cv2.circle(img, (cx, cy), 3, (0, 0, 255), -1)
cv2.circle(img, (cx, cy), korea_radius, (0, 0, 255), 2)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.ellipse(img, ellipse, (50, 50, 50), 2)
# 각 방위의 끝 지점(최 남단, 최 북단,....)
leftmost = tuple(cnt[cnt[:, :, 0].argmin()][0])
rightmost = tuple(cnt[cnt[:, :, 0].argmax()][0])
topmost = tuple(cnt[cnt[:, :, 1].argmin()][0])
bottommost = tuple(cnt[cnt[:, :, 1].argmax()][0])
print('leftmost = ', leftmost)
print('rightmost = ', rightmost)
print('topmost = ', topmost)
print('bottommost = ', bottommost)
img = cv2.line(img, leftmost, leftmost, (10, 10, 10), 10)
img = cv2.line(img, rightmost, rightmost, (10, 10, 10), 10)
img = cv2.line(img, topmost, topmost, (10, 10, 10), 10)
img = cv2.line(img, bottommost, bottommost, (10, 10, 10), 10)
cv2.imshow('Korea Features', img)
impDef.close_window()
def start_tracking(self):
# Iterate until the user presses the Esc key
while True:
# Capture the frame from webcam
_, self.frame = self.cap.read()
# Resize the input frame
self.frame = cv.resize(self.frame,
None,
fx=self.scaling_factor,
fy=self.scaling_factor,
interpolation=cv.INTER_AREA)
# Create a copy of the frame
vis = self.frame.copy()
# Convert the frame to HSV colorspace
hsv = cv.cvtColor(self.frame, cv.COLOR_BGR2HSV)
# Create the mask based on predefined thresholds
mask = cv.inRange(hsv, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
# Check if the user has selected the region
if self.selection:
# Extract the coordinates of the selected rectangle
x0, y0, x1, y1 = self.selection
# Extract the tracking window
self.track_window = (x0, y0, x1 - x0, y1 - y0)
# Extract the regions of interest
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
# Compute the histogram of the region of
# interest in the HSV image using the mask
hist = cv.calcHist([hsv_roi], [0], mask_roi, [16], [0, 180])
# Normalize and reshape the histogram
cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX)
self.hist = hist.reshape(-1)
# Extract the region of interest from the frame
vis_roi = vis[y0:y1, x0:x1]
# Compute the image negative (for display only)
cv.bitwise_not(vis_roi, vis_roi)
vis[mask == 0] = 0
# Check if the system in the "tracking" mode
if self.tracking_state == 1:
# Reset the selection variable
self.selection = None
# Compute the histogram back projection
hsv_backproj = cv.calcBackProject([hsv], [0], self.hist,
[0, 180], 1)
# Compute bitwise AND between histogram
# backprojection and the mask
hsv_backproj &= mask
# Define termination criteria for the tracker
term_crit = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10,
1)
# Apply CAMShift on 'hsv_backproj'
track_box, self.track_window = cv.CamShift(
hsv_backproj, self.track_window, term_crit)
# Draw an ellipse around the object
cv.ellipse(vis, track_box, (0, 255, 0), 2)
# Show the output live video
cv.imshow('Object Tracker', vis)
# Stop if the user hits the 'Esc' key
c = cv.waitKey(5)
if c == 27:
break
# Close all the windows
cv.destroyAllWindows()
# 轉換為整數，還是不太懂int0，網路上說int0=intp（？ box = np.int0(box) cv2.drawContours(originalImage, [box], 0, (0, 0, 255), 5) # 擬合橢圓的最小外接橢圓形 ellipse = cv2.fitEllipse(cntb) ''' fitEllipse回傳5個值 → xc : x coordinate of the center → yc : y coordinate of the center → a : major semi-axis → b : minor semi-axis → theta : rotation angle ''' cv2.ellipse(imgB, ellipse, (255, 0, 255), 5) ''' void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, const Scalar& color, int thickness=1, int lineType=8, int shift=0) → img(imgB)：輸入圖，橢圓會畫在上面。 → center(ellipse)：圓心。 → axes(ellipse)：橢圓軸的尺寸。 → angle(ellipse)：旋轉角度，單位角度。 → startAngle(ellipse)：橢圓弧度起始角度，單位角度。 → endAngle(ellipse)：橢圓弧度結束角度，單位角度。 → color(255, 0, 255)：橢圓的顏色。 → thickness(2)：橢圓的邊線寬度，輸入負值或CV_FILLED代表填滿橢圓形 。 → lineType(*)：通道型態，可輸入8、4、CV_AA： 8->8通道連結。 4->4通道連結。 CV_AA->消除鋸齒(antialiased line)，消除顯示器畫面線邊緣的凹凸鋸齒。 ''' # 擬合最小圓形外框 (x, y), radius = cv2.minEnclosingCircle(cntb)
rotatedRect = cv2.drawContours(img.copy(), [box], -1, (255,255,0),3)
cv2.imshow('rotatedRect',rotatedRect)
# Minimum Enclosing Circle
# Next we find the circumcircle of an object using the function cv2.minEnclosingCircle(). It is a circle which completely covers the object with minimum area.
(x,y),radius = cv2.minEnclosingCircle(cnt)
minEnclosCircle = cv2.circle(img.copy(), (int(x),int(y)), int(radius), (255,255,0), 2)
cv2.imshow('minEnclosCircle',minEnclosCircle)
# Fitting an Ellipse
# Next one is to fit an ellipse to an object. It returns the rotated rectangle in which the ellipse is inscribed.
ellipse = cv2.fitEllipse(cnt)
fitEllipse = cv2.ellipse(img.copy(),ellipse,(0,255,0),2)
cv2.imshow('fitEllipse',fitEllipse)
# Fitting a Line
# Similarly we can fit a line to a set of points. Below image contains a set of white points. We can approximate a straight line to it.
rows,cols = img.shape[:2]
[vx,vy,x,y] = cv2.fitLine(cnt, cv2.DIST_L2,0,0.01,0.01)
lefty = int((-x*vy/vx) + y)
righty = int(((cols-x)*vy/vx)+y)
fitLine = cv2.line(img.copy(),(cols-1,righty),(0,lefty),(0,255,0),2)
cv2.imshow('fitLine',fitLine)
# hold window
cv2.waitKey(0)
cv2.destroyAllWindows()
def elongate_contour(contour, img_shape, extend_length):
c_mask = np.zeros(img_shape, dtype=np.uint8)
cv2.drawContours(c_mask, [contour], -1, 255, -1)
rect = cv2.minAreaRect(contour)
cx, cy = rect[0]
w, h = rect[1]
angle = rect[2]
if w <= 1 or h <= 1 or extend_length < 0:
return contour
if isinstance(extend_length, float) and extend_length <= 1.0:
extend_length = int(extend_length * max(w, h)) + 1
if w > h:
angle = angle - 90
mat = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
c_mask_rot = cv2.warpAffine(c_mask, mat, img_shape)
c_mask_rot[c_mask_rot > 0] = 255
y_locs = np.where(c_mask_rot > 0)[0]
y_min = y_locs.min()
y_max = y_locs.max()
y_mid = int(np.round(np.average([y_min, y_max])))
top_x_locs = np.where(c_mask_rot[y_min + 1, :] > 0)[0]
mid_x_locs = np.where(c_mask_rot[y_mid, :] > 0)[0]
bottom_x_locs = np.where(c_mask_rot[y_max - 1, :] > 0)[0]
mid_x_min = mid_x_locs.min()
mid_x_max = mid_x_locs.max()
mid_x_mid = int(np.round(np.average([mid_x_min, mid_x_max])))
mid_width = mid_x_max - mid_x_min
if len(top_x_locs) > 0:
top_x_min = top_x_locs.min()
top_x_max = top_x_locs.max()
top_x_mid = int(np.round(np.average([top_x_min, top_x_max])))
extend_top = True
else:
extend_top = False
if len(bottom_x_locs) > 0:
bottom_x_min = bottom_x_locs.min()
bottom_x_max = bottom_x_locs.max()
bottom_x_mid = int(np.round(np.average([bottom_x_min, bottom_x_max])))
extend_bottom = True
else:
extend_bottom = False
mid_coord = (mid_x_mid, y_mid)
new_c_mask_rot = c_mask_rot.copy()
if extend_top:
top_coord = (top_x_mid, y_min)
top_angle = np.math.atan2(top_coord[1] - mid_coord[1],
top_coord[0] - mid_coord[0])
top_angle = top_angle * 180 / np.pi
cv2.ellipse(new_c_mask_rot, top_coord,
(extend_length, int(mid_width / 4)), top_angle, 0, 360,
255, -1)
if extend_bottom:
bottom_coord = (bottom_x_mid, y_max)
bottom_angle = np.math.atan2(bottom_coord[1] - mid_coord[1],
bottom_coord[0] - mid_coord[0])
bottom_angle = bottom_angle * 180 / np.pi
cv2.ellipse(new_c_mask_rot, bottom_coord,
(extend_length, int(mid_width / 4)), bottom_angle, 0, 360,
255, -1)
inv_mat = cv2.getRotationMatrix2D((cx, cy), -angle, 1.0)
c_mask_new = cv2.warpAffine(new_c_mask_rot, inv_mat, img_shape)
# fix interpolation artifacts
c_mask_new[c_mask_new > 0] = 255
contours, hierarchy = cv2.findContours(c_mask_new.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
return contours[0]
# -*- coding: utf-8 -*-
# !/usr/bin/env python
import numpy as np
from cv2 import cv2 as cv
# 创建黑色的图像
img = np.zeros((512, 512, 3), np.uint8)
# 绘制一条厚度为5的蓝色对角线
cv.line(img, (0, 0), (511, 511), (255, 0, 0), 5)
cv.imshow("geometric", img)
cv.waitKey(0)
cv.destroyAllWindows()
# 创建黑色的图像
circle = np.zeros((512, 512, 3), np.uint8)
# 绘制 圆
cv.circle(circle, (447, 63), 63, (0, 0, 255), -1)
cv.imshow("circle", circle)
cv.waitKey(0)
cv.destroyAllWindows()
# 创建黑色的图像
ellipse = np.zeros((512, 512, 3), np.uint8)
# 绘制 椭圆
cv.ellipse(ellipse, (256, 256), (100, 50), 0, 0, 180, 255, -1)
cv.imshow("ellipse", ellipse)
cv.waitKey(0)
cv.destroyAllWindows()
# 图片 起点 终点 颜色RGB 粗细 cv2.line(img, (0, 0), (511, 511), (255, 255, 0), 1) ''' 画矩形 ''' cv2.rectangle(img, (0, 0), (510, 128), (0, 255, 0), 2) ''' 画圆 ''' # 圆心 半径 颜色 -1表实心 cv2.circle(img, (447, 63), 63, (0, 0, 255), -1) ''' 画椭圆 ''' # （中心点）（水平轴 垂直轴），角度，画出的起始度数，结束度数，颜色，粗细 cv2.ellipse(img, (256, 256), (100, 50), 0, 10, 360, (0, 255, 0), -1) ''' 画多边形 ''' pts = np.array([[400, 5], [200, 300], [150, 20], [500, 100]], np.int32) pts = pts.reshape((-1, 1, 2)) cv2.polylines(img, [pts], True, (255, 125, 0)) # 这里 reshape 的第一个参数为-1, 表明这一维的长度是根据后面的维度的计算出来的。 ''' 添加文字 ''' font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(img, 'LESVAY', (10, 500), font, 4, (255, 255, 255), 2) ''' 显示结果 '''
#arrowedline
img = cv2.arrowedLine(img, (0, 255), (255, 255), (255, 0, 0), 10)
#rectangle
img = cv2.rectangle(img, (344, 0), (418, 120), (0, 255, 0),
-1) #-1 gives filled shape
#circle
img = cv2.circle(img, (210, 210), 50, (217, 200, 0), 10)
#put text on image
font = cv2.FONT_HERSHEY_COMPLEX
img = cv2.putText(img, 'Lena', (300, 350), font, 2, (255, 255, 0), 2,
cv2.LINE_AA)
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
#Create blank image
image = np.zeros([512, 512, 3], np.uint8)
#draw ellipse
image = cv2.ellipse(image, (300, 300), (100, 50), 0, 0, 180, (255, 0, 0), -1)
#draw polygon
pts = np.array([[10, 5], [20, 30], [70, 20], [50, 10]], np.int32)
pts = pts.reshape((-1, 1, 2))
cv2.polylines(image, [pts], True, (0, 255, 255))
#put text
cv2.putText(image, 'OpenCV', (100, 200), font, 2, (128, 200, 40), 5,
cv2.LINE_8)
cv2.imshow("image1", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
from cv2 import cv2
import numpy as np
# If you're wondering why only two dimensions, well this is a grayscale image,
# if we doing a colored image, we'd use
# rectangle = np.zeros((300, 300, 3), np.uint8)
# Making a square
square = np.zeros((300, 300), np.uint8)
cv2.rectangle(square, (50, 50), (250, 250), 255, -2)
cv2.imshow("Square", square)
cv2.waitKey()
# Making a ellipse
ellipse = np.zeros((300, 300), np.uint8)
cv2.ellipse(ellipse, (150, 150), (150, 150), 30, 0, 180, 255, -1)
cv2.imshow("Ellipse", ellipse)
cv2.waitKey()
cv2.destroyAllWindows()
# Shows only were they intersect
And = cv2.bitwise_and(square, ellipse)
cv2.imshow("AND", And)
cv2.waitKey()
# Shows where either square or ellipse is
bitwiseOr = cv2.bitwise_or(square, ellipse)
cv2.imshow("OR", bitwiseOr)
# Shows where either exist by itself
#将图片输入到caffe神经网络中
net.setInput(inp)
#输出前向传播的预测结果
out = net.forward()
points = []
for i in range(len(BODY_PARTS)):
heatMap = out[0, i, :, :]
_, conf, _, point = cv.minMaxLoc(heatMap)
x = (frameWidth * point[0]) / out.shape[3]
y = (frameHeight * point[1]) / out.shape[2]
points.append((int(x), int(y)) if conf > 0.1 else None)
for pair in POSE_PAIRS:
partFrom = pair[0]
partTo = pair[1]
assert (partFrom in BODY_PARTS)
assert (partTo in BODY_PARTS)
idFrom = BODY_PARTS[partFrom]
idTo = BODY_PARTS[partTo]
if points[idFrom] and points[idTo]:
cv.line(frame, points[idFrom], points[idTo], (0, 255, 0), 3)
cv.ellipse(frame, points[idFrom], (3, 3), 0, 0, 360, (0, 0, 255),
cv.FILLED)
cv.ellipse(frame, points[idTo], (3, 3), 0, 0, 360, (0, 0, 255),
cv.FILLED)
cv.imshow('GuYuPose', frame)
continue
if radius == 0:
continue
#面积过滤
areaex = np.pi * a * b
area = cv2.contourArea(cnt)
ratio = area / areaex
if ratio < thresh_upper and ratio > thresh_lower:
cntsex.append(cnt)
show = cv2.drawContours(show, [cnt], 0, (0, 255, 0), 1)
show = cv2.ellipse(show, center, (int(a), int(b)), 0, 0, 360, (0, 0, 255),
1)
#第二次过滤
cnts = []
maxarea = -1e6
for cnt in cntsex:
area = cv2.contourArea(cnt)
if area > maxarea:
maxarea = area
maxgap = 0.5 * maxarea
cntsex = sorted(cntsex, key=lambda x: cv2.contourArea(x), reverse=True)
import numpy as np
from cv2 import cv2
# Create a black image
img = np.zeros((512,512,3), np.uint8)
img = cv2.line(img,(0,0),(511,511),(0,0,255),1,cv2.LINE_AA)
img = cv2.rectangle(img,(384,0),(510,128),(0,255,0),3)
img = cv2.circle(img,(447,63), 63, (0,0,255), -1)
img = cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1)
pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)
pts = pts.reshape((-1,1,2))
img = cv2.polylines(img,[pts],True,(0,255,255))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv2.LINE_AA)
# using cv show
cv2.imshow('hello', img)
# hold window
cv2.waitKey(0)
cv2.destroyAllWindows()
def circle_line(origin, thresed, S_thre=(100000, 600000)):
# imgray = cv2.Canny(img, 300, 120, 3)
# ret, thresh = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY)
# cv2.imshow("thres", thresh)
# image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# _, contours, _ = cv2.findContours(thresed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours, _ = cv2.findContours(thresed, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
top = []
bottle = []
right = []
left = []
center = []
Circle_2D = []
last_longest = 4
des_ell = tuple()
for cnt in contours:
if len(cnt) > last_longest: # 确保输出点数最多（且面积不超标）的椭圆
ell = cv2.fitEllipse(
cnt) # 返回椭圆所在的矩形框# 函数返回值为：椭圆的中心坐标，长短轴长度（2a，2b），旋转角度
S2 = math.pi * ell[1][0] * ell[1][1] # 椭圆面积
if S2 >= S_thre[0] and S2 <= S_thre[1]: # 过滤面积小的
last_longest = len(cnt)
des_ell = ell
top_x = ell[0][0] - 0.5 * ell[1][0] * math.cos(
math.radians(ell[2]))
top_y = ell[0][1] - 0.5 * ell[1][0] * math.sin(
math.radians(ell[2]))
bottle_x = ell[0][0] + 0.5 * ell[1][0] * math.cos(
math.radians(ell[2]))
bottle_y = ell[0][1] + 0.5 * ell[1][0] * math.sin(
math.radians(ell[2]))
right_x = ell[0][0] + 0.5 * ell[1][1] * math.sin(
math.radians(ell[2]))
right_y = ell[0][1] - 0.5 * ell[1][1] * math.cos(
math.radians(ell[2]))
left_x = ell[0][0] - 0.5 * ell[1][1] * math.sin(
math.radians(ell[2]))
left_y = ell[0][1] + 0.5 * ell[1][1] * math.cos(
math.radians(ell[2]))
top = (int(top_x), int(top_y))
bottle = (int(bottle_x), int(bottle_y))
right = (int(right_x), int(right_y))
left = (int(left_x), int(left_y))
center = (int(ell[0][0]), int(ell[0][1]))
origin = cv2.ellipse(origin, des_ell, (0, 255, 0), 2) # 绘制椭圆
drawpoi(origin, top)
drawpoi(origin, bottle)
drawpoi(origin, right)
drawpoi(origin, left)
drawpoi(origin, center)
# rate = ell[1][1]/ell[1][0]
# elif S2< S_thre[1]:
# print("\033[0;31m椭圆面积过小！\033[0m")
# elif S2 > S_thre[0]:
# print("\033[0;31m椭圆面积过大！\033[0m")
# else:
# print("\033[0;31m未检测到圆桌！\033[0m")
# top = [0, 0]; bottle = [0, 0]; right = [0, 0]; left = [0, 0]; center = [0, 0]
Circle_2D.append(top)
Circle_2D.append(bottle)
Circle_2D.append(right)
Circle_2D.append(left)
Circle_2D = np.array(Circle_2D, dtype='float32')
return origin, thresed, Circle_2D
