本文整理汇总了Python中cv2.boundingRect方法的典型用法代码示例。如果您正苦于以下问题:Python cv2.boundingRect方法的具体用法?Python cv2.boundingRect怎么用?Python cv2.boundingRect使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv2
的用法示例。
在下文中一共展示了cv2.boundingRect方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: canny
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def canny(filepathname, left=70, right=140):
v = cv2.imread(filepathname)
s = cv2.cvtColor(v, cv2.COLOR_BGR2GRAY)
s = cv2.Canny(s, left, right)
cv2.imshow('nier',s)
return s
# 圈出最小方矩形框,这里Canny算法后都是白色线条,所以取色范围 127-255 即可。
# ret, binary = cv2.threshold(s,127,255,cv2.THRESH_BINARY)
# contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# for c in contours:
# x,y,w,h = cv2.boundingRect(c)
# if w>5 and h>10: # 有约束的画框
# cv2.rectangle(v,(x,y),(x+w,y+h),(155,155,0),1)
# # cv2.drawContours(s,contours,-1,(0,0,255),3) # 画所有框
# cv2.imshow('nier2',v)
# cv2.waitKey()
# cv2.destroyAllWindows()
示例2: _find_size_candidates
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def _find_size_candidates(self, image):
binary_image = self._filter_image(image)
_, contours, _ = cv2.findContours(binary_image,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
size_candidates = []
for contour in contours:
bounding_rect = cv2.boundingRect(contour)
contour_area = cv2.contourArea(contour)
if self._is_valid_contour(contour_area, bounding_rect):
candidate = (bounding_rect[2] + bounding_rect[3]) / 2
size_candidates.append(candidate)
return size_candidates
示例3: laplacian
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def laplacian(filepathname):
v = cv2.imread(filepathname)
s = cv2.cvtColor(v, cv2.COLOR_BGR2GRAY)
s = cv2.Laplacian(s, cv2.CV_16S, ksize=3)
s = cv2.convertScaleAbs(s)
cv2.imshow('nier',s)
return s
# ret, binary = cv2.threshold(s,40,255,cv2.THRESH_BINARY)
# contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# for c in contours:
# x,y,w,h = cv2.boundingRect(c)
# if w>5 and h>10:
# cv2.rectangle(v,(x,y),(x+w,y+h),(155,155,0),1)
# cv2.imshow('nier2',v)
# cv2.waitKey()
# cv2.destroyAllWindows()
示例4: __get_annotation__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def __get_annotation__(self, mask, image=None):
_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
segmentation = []
for contour in contours:
# Valid polygons have >= 6 coordinates (3 points)
if contour.size >= 6:
segmentation.append(contour.flatten().tolist())
RLEs = cocomask.frPyObjects(segmentation, mask.shape[0], mask.shape[1])
RLE = cocomask.merge(RLEs)
# RLE = cocomask.encode(np.asfortranarray(mask))
area = cocomask.area(RLE)
[x, y, w, h] = cv2.boundingRect(mask)
if image is not None:
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.drawContours(image, contours, -1, (0,255,0), 1)
cv2.rectangle(image,(x,y),(x+w,y+h), (255,0,0), 2)
cv2.imshow("", image)
cv2.waitKey(1)
return segmentation, [x, y, w, h], area
示例5: _append_boxes_from_saliency
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def _append_boxes_from_saliency(self, proto_objects_map, box_all):
"""Adds to the list all bounding boxes found with the saliency map
A saliency map is used to find objects worth tracking in each
frame. This information is combined with a mean-shift tracker
to find objects of relevance that move, and to discard everything
else.
:param proto_objects_map: proto-objects map of the current frame
:param box_all: append bounding boxes from saliency to this list
:returns: new list of all collected bounding boxes
"""
# find all bounding boxes in new saliency map
box_sal = []
cnt_sal, _ = cv2.findContours(proto_objects_map, 1, 2)
for cnt in cnt_sal:
# discard small contours
if cv2.contourArea(cnt) < self.min_cnt_area:
continue
# otherwise add to list of boxes found from saliency map
box = cv2.boundingRect(cnt)
box_all.append(box)
return box_all
示例6: filter_prediction
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def filter_prediction(self, output, image):
if len(output) < 2:
return pd.DataFrame()
else:
df = pd.DataFrame(output)
df = df.assign(
area=lambda x: df[0].apply(lambda x: cv2.contourArea(x)),
bounding=lambda x: df[0].apply(lambda x: cv2.boundingRect(x))
)
df = df[df['area'] > MIN_AREA]
df_filtered = pd.DataFrame(
df['bounding'].values.tolist(), columns=['x1', 'y1', 'w', 'h'])
df_filtered = df_filtered.assign(
x1=lambda x: x['x1'].clip(0),
y1=lambda x: x['y1'].clip(0),
x2=lambda x: (x['x1'] + x['w']),
y2=lambda x: (x['y1'] + x['h']),
label=lambda x: x.index.astype(str),
class_name=lambda x: x.index.astype(str),
)
return df_filtered
示例7: merge_img
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def merge_img(src_img, dst_img, dst_matrix, dst_points, blur_detail_x=None, blur_detail_y=None, mat_multiple=None):
face_mask = np.zeros(src_img.shape, dtype=src_img.dtype)
for group in core.OVERLAY_POINTS:
cv2.fillConvexPoly(face_mask, cv2.convexHull(dst_matrix[group]), (255, 255, 255))
r = cv2.boundingRect(np.float32([dst_points[:core.FACE_END]]))
center = (r[0] + int(r[2] / 2), r[1] + int(r[3] / 2))
if mat_multiple:
mat = cv2.getRotationMatrix2D(center, 0, mat_multiple)
face_mask = cv2.warpAffine(face_mask, mat, (face_mask.shape[1], face_mask.shape[0]))
if blur_detail_x and blur_detail_y:
face_mask = cv2.blur(face_mask, (blur_detail_x, blur_detail_y), center)
return cv2.seamlessClone(np.uint8(dst_img), src_img, face_mask, center, cv2.NORMAL_CLONE)
示例8: mark_hand_center
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def mark_hand_center(frame_in,cont):
max_d=0
pt=(0,0)
x,y,w,h = cv2.boundingRect(cont)
for ind_y in xrange(int(y+0.3*h),int(y+0.8*h)): #around 0.25 to 0.6 region of height (Faster calculation with ok results)
for ind_x in xrange(int(x+0.3*w),int(x+0.6*w)): #around 0.3 to 0.6 region of width (Faster calculation with ok results)
dist= cv2.pointPolygonTest(cont,(ind_x,ind_y),True)
if(dist>max_d):
max_d=dist
pt=(ind_x,ind_y)
if(max_d>radius_thresh*frame_in.shape[1]):
thresh_score=True
cv2.circle(frame_in,pt,int(max_d),(255,0,0),2)
else:
thresh_score=False
return frame_in,pt,max_d,thresh_score
# 6. Find and display gesture
示例9: getFeatures
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def getFeatures(img,bbox,use_shi=False):
n_object = np.shape(bbox)[0]
N = 0
temp = np.empty((n_object,),dtype=np.ndarray) # temporary storage of x,y coordinates
for i in range(n_object):
(xmin, ymin, boxw, boxh) = cv2.boundingRect(bbox[i,:,:].astype(int))
roi = img[ymin:ymin+boxh,xmin:xmin+boxw]
# cv2.imshow('roi',roi)
if use_shi:
corner_response = corner_shi_tomasi(roi)
else:
corner_response = corner_harris(roi)
coordinates = peak_local_max(corner_response,num_peaks=20,exclude_border=2)
coordinates[:,1] += xmin
coordinates[:,0] += ymin
temp[i] = coordinates
if coordinates.shape[0] > N:
N = coordinates.shape[0]
x = np.full((N,n_object),-1)
y = np.full((N,n_object),-1)
for i in range(n_object):
n_feature = temp[i].shape[0]
x[0:n_feature,i] = temp[i][:,1]
y[0:n_feature,i] = temp[i][:,0]
return x,y
示例10: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def __init__(self, _contour):
self.contour = _contour
self.boundingRect = cv2.boundingRect(self.contour)
[intX, intY, intWidth, intHeight] = self.boundingRect
self.intBoundingRectX = intX
self.intBoundingRectY = intY
self.intBoundingRectWidth = intWidth
self.intBoundingRectHeight = intHeight
self.intBoundingRectArea = self.intBoundingRectWidth * self.intBoundingRectHeight
self.intCenterX = (self.intBoundingRectX + self.intBoundingRectX + self.intBoundingRectWidth) / 2
self.intCenterY = (self.intBoundingRectY + self.intBoundingRectY + self.intBoundingRectHeight) / 2
self.fltDiagonalSize = math.sqrt((self.intBoundingRectWidth ** 2) + (self.intBoundingRectHeight ** 2))
self.fltAspectRatio = float(self.intBoundingRectWidth) / float(self.intBoundingRectHeight)
# end constructor
# end class
示例11: calcSafeRect
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def calcSafeRect(self, roi, src):
'''
return [x, y, w, h]
'''
box = cv2.boxPoints(roi)
x, y, w, h = cv2.boundingRect(box)
src_h, src_w, _ = src.shape
tl_x = x if x > 0 else 0
tl_y = y if y > 0 else 0
br_x = x + w - 1 if x + w - 1 < src_w else src_w - 1
br_y = y + h - 1 if y + h - 1 < src_h else src_h - 1
roi_w = br_x - tl_x
roi_h = br_y - tl_y
if roi_w <= 0 or roi_h <= 0:
return [tl_x, tl_y, roi_w, roi_h], False
return [tl_x, tl_y, roi_w, roi_h], True
示例12: sobel
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def sobel(filepathname):
v = cv2.imread(filepathname)
s = cv2.cvtColor(v,cv2.COLOR_BGR2GRAY)
x, y = cv2.Sobel(s,cv2.CV_16S,1,0), cv2.Sobel(s,cv2.CV_16S,0,1)
s = cv2.convertScaleAbs(cv2.subtract(x,y))
s = cv2.blur(s,(9,9))
cv2.imshow('nier',s)
return s
# ret, binary = cv2.threshold(s,40,255,cv2.THRESH_BINARY)
# contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# for c in contours:
# x,y,w,h = cv2.boundingRect(c)
# if w>5 and h>10:
# cv2.rectangle(v,(x,y),(x+w,y+h),(155,155,0),1)
# cv2.imshow('nier2',v)
# cv2.waitKey()
# cv2.destroyAllWindows()
示例13: tightboundingbox
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def tightboundingbox(self, image):
ret, thresh = cv2.threshold(np.array(image, dtype=np.uint8), 0, 255, 0)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
bb = []
for c in contours:
x, y, w, h = cv2.boundingRect(c)
# +1 is done to encapsulate entire figure
w += 2
h += 2
x -= 1
y -= 1
x = np.max([0, x])
y = np.max([0, y])
bb.append([y, x, w, h])
bb = self.nms(bb)
return bb
示例14: affine_skew
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def affine_skew(self, tilt, phi, img, mask=None):
h, w = img.shape[:2]
if mask is None:
mask = np.zeros((h, w), np.uint8)
mask[:] = 255
A = np.float32([[1, 0, 0], [0, 1, 0]])
if phi != 0.0:
phi = np.deg2rad(phi)
s, c = np.sin(phi), np.cos(phi)
A = np.float32([[c, -s], [s, c]])
corners = [[0, 0], [w, 0], [w, h], [0, h]]
tcorners = np.int32(np.dot(corners, A.T))
x, y, w, h = cv2.boundingRect(tcorners.reshape(1, -1, 2))
A = np.hstack([A, [[-x], [-y]]])
img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
if tilt != 1.0:
s = 0.8*np.sqrt(tilt * tilt - 1)
img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv2.resize(img, (0, 0), fx=1.0 / tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
A[0] /= tilt
if phi != 0.0 or tilt != 1.0:
h, w = img.shape[:2]
mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
Ai = cv2.invertAffineTransform(A)
return img, mask, Ai
示例15: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import boundingRect [as 别名]
def __init__(self, cntr):
self.contour = cntr
self.boundingRect = cv2.boundingRect(self.contour)
[x, y, w, h] = self.boundingRect
self.boundingRectX = x
self.boundingRectY = y
self.boundingRectWidth = w
self.boundingRectHeight = h
self.boundingRectArea = self.boundingRectWidth * self.boundingRectHeight
self.centerX = (self.boundingRectX + self.boundingRectX + self.boundingRectWidth) / 2
self.centerY = (self.boundingRectY + self.boundingRectY + self.boundingRectHeight) / 2
self.diagonalSize = math.sqrt((self.boundingRectWidth ** 2) + (self.boundingRectHeight ** 2))
self.aspectRatio = float(self.boundingRectWidth) / float(self.boundingRectHeight)