Draw Image X Y Circle
Python cv2.circle() Examples
The post-obit are 30 code examples for showing how to use cv2.circle() . These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't similar, and go to the original project or source file by following the links in a higher place each example.
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Example 1
def update(_=None): noise = cv2.getTrackbarPos('dissonance', 'fit line') n = cv2.getTrackbarPos('indicate due north', 'fit line') r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0 outn = int(n*r) p0, p1 = (90, 80), (westward-90, h-80) img = np.zeros((h, westward, 3), np.uint8) cv2.line(img, toint(p0), toint(p1), (0, 255, 0)) if n > 0: line_points = sample_line(p0, p1, n-outn, noise) outliers = np.random.rand(outn, 2) * (w, h) points = np.vstack([line_points, outliers]) for p in line_points: cv2.circle(img, toint(p), ii, (255, 255, 255), -1) for p in outliers: cv2.circle(img, toint(p), 2, (64, 64, 255), -1) func = getattr(cv2, cur_func_name) vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01) cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*west), int(cy+vy*westward)), (0, 0, 255)) draw_str(img, (20, 20), cur_func_name) cv2.imshow('fit line', img) Instance 2
def ProcessFrame(self, frame): # segment arm region segment = cocky.SegmentArm(frame) # brand a re-create of the segmented image to draw on draw = cv2.cvtColor(segment, cv2.COLOR_GRAY2RGB) # draw some helpers for correctly placing mitt cv2.circle(draw,(self.imgWidth/2,self.imgHeight/2),3,[255,102,0],2) cv2.rectangle(draw, (cocky.imgWidth/3,self.imgHeight/3), (self.imgWidth*2/3, self.imgHeight*2/3), [255,102,0],two) # find the hull of the segmented area, and based on that observe the # convexity defects [contours,defects] = self.FindHullDefects(segment) # detect the number of fingers depending on the contours and convexity defects # draw defects that belong to fingers dark-green, others red [nofingers,depict] = self.DetectNumberFingers(contours, defects, draw) # impress number of fingers on prototype cv2.putText(draw, str(nofingers), (30,30), cv2.FONT_HERSHEY_SIMPLEX, i, (255,255,255)) return draw
Example 3
def hulk(x): """Given an Nx3 matrix of hulk positions and size, create N img_size x img_size images, each with a hulk fatigued on them given by the value in each row of x One row of x = [x,y,radius].""" y = np.zeros((ten.shape[0], img_size, img_size)) for i, particle in enumerate(x): rr, cc = skimage.draw.circle( particle[0], particle[1], max(particle[two], 1), shape=(img_size, img_size) ) y[i, rr, cc] = 1 return y #%% # names (this is just for reference for the moment!)
Instance 4
def update(self, radarData): self.img = np.zeros((self.top, cocky.width, self.channels), np.uint8) cv2.line(self.img, (ten, 0), (cocky.width/2 - 5, self.height), (100, 255, 255)) cv2.line(self.img, (self.width - 10, 0), (cocky.width/ii + 5, self.elevation), (100, 255, 255)) for track_number in range(1, 65): if str(track_number)+'_track_range' in radarData: track_range = radarData[str(track_number)+'_track_range'] track_angle = (float(radarData[str(track_number)+'_track_angle'])+90.0)*math.pi/180 x_pos = math.cos(track_angle)*track_range*iv y_pos = math.sin(track_angle)*track_range*iv cv2.circumvolve(self.img, (self.width/2 + int(x_pos), self.height - int(y_pos) - ten), 5, (255, 255, 255)) #cv2.putText(cocky.img, str(track_number), # (cocky.width/two + int(x_pos)-2, self.pinnacle - int(y_pos) - 10), self.font, 1, (255,255,255), two) cv2.imshow("Radar", self.img) cv2.waitKey(ii) Example 5
def update(self, radarData): cocky.img = np.zeros((cocky.top, self.width, self.channels), np.uint8) cv2.line(cocky.img, (10, 0), (self.width/2 - 5, self.height), (100, 255, 255)) cv2.line(self.img, (self.width - x, 0), (self.width/2 + v, self.height), (100, 255, 255)) for track_number in range(1, 65): if str(track_number)+'_track_range' in radarData: track_range = radarData[str(track_number)+'_track_range'] track_angle = (float(radarData[str(track_number)+'_track_angle'])+90.0)*math.pi/180 x_pos = math.cos(track_angle)*track_range*iv y_pos = math.sin(track_angle)*track_range*four cv2.circle(self.img, (self.width/ii + int(x_pos), cocky.height - int(y_pos) - 10), 5, (255, 255, 255)) #cv2.putText(cocky.img, str(track_number), # (self.width/two + int(x_pos)-ii, self.height - int(y_pos) - 10), self.font, 1, (255,255,255), 2) cv2.imshow("Radar", cocky.img) cv2.waitKey(ii) Example 6
def mark_hand_center(frame_in,cont): max_d=0 pt=(0,0) 10,y,w,h = cv2.boundingRect(cont) for ind_y in xrange(int(y+0.three*h),int(y+0.8*h)): #effectually 0.25 to 0.vi region of height (Faster calculation with ok results) for ind_x in xrange(int(x+0.3*due west),int(x+0.6*due west)): #around 0.3 to 0.6 region of width (Faster adding 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[i]): 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 # half dozen. Find and brandish gesture
Instance 7
def draw_humans(npimg, humans, imgcopy=Faux): if imgcopy: npimg = np.copy(npimg) image_h, image_w = npimg.shape[:2] centers = {} for human in humans: # describe signal for i in range(common.CocoPart.Background.value): if i not in man.body_parts.keys(): continue body_part = human being.body_parts[i] center = (int(body_part.ten * image_w + 0.v), int(body_part.y * image_h + 0.5)) centers[i] = middle cv2.circle(npimg, center, 3, common.CocoColors[i], thickness=iii, lineType=8, shift=0) # describe line for pair_order, pair in enumerate(common.CocoPairsRender): if pair[0] not in homo.body_parts.keys() or pair[1] non in human.body_parts.keys(): go along npimg = cv2.line(npimg, centers[pair[0]], centers[pair[ane]], common.CocoColors[pair_order], 3) return npimg Instance 8
def draw_limbs(prototype, pose_2d, visible): """Draw the 2D pose without the occluded/not visible joints.""" _COLORS = [ [0, 0, 255], [0, 170, 255], [0, 255, 170], [0, 255, 0], [170, 255, 0], [255, 170, 0], [255, 0, 0], [255, 0, 170], [170, 0, 255] ] _LIMBS = np.array([0, 1, two, 3, 3, iv, 5, 6, 6, 7, 8, nine, 9, 10, xi, 12, 12, 13]).reshape((-1, 2)) _NORMALISATION_FACTOR = int(math.flooring(math.sqrt(image.shape[0] * image.shape[1] / NORMALISATION_COEFFICIENT))) for oid in range(pose_2d.shape[0]): for lid, (p0, p1) in enumerate(_LIMBS): if not (visible[oid][p0] and visible[oid][p1]): continue y0, x0 = pose_2d[oid][p0] y1, x1 = pose_2d[oid][p1] cv2.circumvolve(image, (x0, y0), JOINT_DRAW_SIZE *_NORMALISATION_FACTOR , _COLORS[lid], -ane) cv2.circle(image, (x1, y1), JOINT_DRAW_SIZE*_NORMALISATION_FACTOR , _COLORS[lid], -1) cv2.line(paradigm, (x0, y0), (x1, y1), _COLORS[hat], LIMB_DRAW_SIZE*_NORMALISATION_FACTOR , 16)
Example nine
def plot_3d_pts(img, pts, center, calib_file=None, cam_to_img=None, relative=False, constraint_idx=None): if calib_file is not None: cam_to_img = get_calibration_cam_to_image(calib_file) for pt in pts: if relative: pt = [i + center[j] for j,i in enumerate(pt)] # more pythonic indicate = project_3d_pt(pt, cam_to_img) color = cv_colors.RED.value if constraint_idx is not None: color = constraint_to_color(constraint_idx) cv2.circle(img, (point[0], point[1]), 3, color, thickness=-1)
Example 10
def annotate(cocky, img): # paint annotations on the paradigm for i1 in range(len(self.kpus)): u1, v1 = int(round(self.kpus[i1][0])), int(round(self.kpus[i1][ane])) if cocky.pts[i1] is non None: if len(self.pts[i1].frames) >= 5: cv2.circle(img, (u1, v1), color=(0,255,0), radius=3) else: cv2.circle(img, (u1, v1), color=(0,128,0), radius=iii) # draw the trail pts = [] lfid = None for f, idx in naught(self.pts[i1].frames[-9:][::-1], cocky.pts[i1].idxs[-nine:][::-1]): if lfid is not None and lfid-1 != f.id: break pts.append(tuple(map(lambda x: int(round(x)), f.kpus[idx]))) lfid = f.id if len(pts) >= ii: cv2.polylines(img, np.assortment([pts], dtype=np.int32), False, myjet[len(pts)]*255, thickness=1, lineType=16) else: cv2.circle(img, (u1, v1), colour=(0,0,0), radius=3) render img # inverse of intrinsics matrix
Case eleven
def load_shapes(self, count, height, width): """Generate the requested number of synthetic images. count: number of images to generate. top, width: the size of the generated images. """ # Add classes cocky.add_class("shapes", 1, "square") self.add_class("shapes", two, "circumvolve") cocky.add_class("shapes", 3, "triangle") # Add images # Generate random specifications of images (i.e. color and # list of shapes sizes and locations). This is more than compact than # actual images. Images are generated on the fly in load_image(). for i in range(count): bg_color, shapes = self.random_image(height, width) self.add_image("shapes", image_id=i, path=None, width=width, pinnacle=height, bg_color=bg_color, shapes=shapes) Example 12
def draw_shape(self, paradigm, shape, dims, colour): """Draws a shape from the given specs.""" # Go the heart x, y and the size s x, y, southward = dims if shape == 'square': image = cv2.rectangle(image, (x - s, y - s), (10 + s, y + s), color, -ane) elif shape == "circumvolve": image = cv2.circumvolve(image, (ten, y), s, color, -one) elif shape == "triangle": points = np.array([[(ten, y - south), (ten - s / math.sin(math.radians(60)), y + due south), (x + southward / math.sin(math.radians(threescore)), y + south), ]], dtype=np.int32) image = cv2.fillPoly(image, points, color) return image
Example 13
def random_shape(cocky, height, width): """Generates specifications of a random shape that lies within the given height and width boundaries. Returns a tuple of 3 valus: * The shape name (square, circle, ...) * Shape colour: a tuple of 3 values, RGB. * Shape dimensions: A tuple of values that ascertain the shape size and location. Differs per shape blazon. """ # Shape shape = random.option(["square", "circle", "triangle"]) # Color colour = tuple([random.randint(0, 255) for _ in range(3)]) # Center ten, y buffer = 20 y = random.randint(buffer, elevation - buffer - i) x = random.randint(buffer, width - buffer - 1) # Size s = random.randint(buffer, height // 4) render shape, color, (ten, y, due south)
Example fourteen
def drawMatch(img0,img1,src,tgt,color='b'): if len(img0.shape)==ii: img0=np.expand_dims(img0,ii) if len(img1.shape)==2: img1=np.expand_dims(img1,2) h,w = img0.shape[0],img0.shape[1] img = np.zeros([ii*h,w,3]) img[:h,:,:] = img0 img[h:,:,:] = img1 due north = len(src) if colour == 'b': color=(255,0,0) else: color=(0,255,0) for i in range(northward): cv2.circle(img, (int(src[i,0]), int(src[i,one])), 3,colour,-i) cv2.circle(img, (int(tgt[i,0]), int(tgt[i,1])+h), 3,colour,-1) cv2.line(img, (int(src[i,0]),int(src[i,1])),(int(tgt[i,0]),int(tgt[i,i])+h),color,one) return img
Example 15
def visualize_joints(bone_list, focus): m = np.zeros((424, 600, 3)) m.astype(np.uint8) for bone in bone_list: p1x = bone[0][0] p1y = bone[0][1] p1z = os[0][ii] + 400 p2x = bone[1][0] p2y = bone[ane][1] p2z = os[1][2] + 400 p1 = ( int(p1x * focus / p1z + 300.0), int(-p1y * focus / p1z + 204.0)) p2 = (int(p2x * focus / p2z + 300.0), int(-p2y * focus / p2z + 204.0)) if inside_image(p1[0], p1[1]) and inside_image(p2[0], p2[1]): cv.line(1000, p1, p2, (255, 0, 0), 2) cv.circle(m, p1, ii, (0, 255, 255), -1) cv.circle(1000, p2, 2, (0, 255, 255), -i) return chiliad
Case 16
def visualize_joints2(bone_list, focus): m = np.zeros((424, 600, 3)) m.astype(np.uint8) for bone in bone_list: p1x = bone[0][0] p1y = bone[0][1] p1z = bone[0][two] + 400 p2x = bone[i][0] p2y = bone[one][one] p2z = bone[one][two] + 400 p1 = ( int(p1x * focus / p1z + 300.0), int(-p1y * focus / p1z + 204.0)) p2 = (int(p2x * focus / p2z + 300.0), int(-p2y * focus / p2z + 204.0)) if inside_image(p1[0], p1[i]) and inside_image(p2[0], p2[ane]): cv.line(m, p1, p2, (255, 0, 0), ii) cv.circle(m, p1, 2, (0, 255, 255), -1) cv.circumvolve(m, p2, ii, (0, 255, 255), -1) return m
Example 17
def plot_kpt(image, kpt): ''' Depict 68 central points Args: image: the input image kpt: (68, 3). ''' epitome = image.re-create() kpt = np.circular(kpt).astype(np.int32) for i in range(kpt.shape[0]): st = kpt[i, :2] prototype = cv2.circumvolve(image,(st[0], st[one]), 1, (0,0,255), 2) if i in end_list: continue ed = kpt[i + 1, :2] epitome = cv2.line(prototype, (st[0], st[ane]), (ed[0], ed[1]), (255, 255, 255), 1) return image
Example 18
def add_coco_hp(image, points, color): for j in range(17): cv2.circumvolve(paradigm, (points[j, 0], points[j, 1]), 2, (int(colour[0]), int(color[ane]), int(color[2])), -i) stickwidth = 2 cur_canvas = paradigm.copy() for j, e in enumerate(_kp_connections): if points[east].min() > 0: X = [points[e[0], ane], points[e[ane], 1]] Y = [points[e[0], 0], points[e[1], 0]] mX = np.mean(X) mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** two) ** 0.v bending = math.degrees(math.atan2(X[0] - Ten[ane], Y[0] - Y[1])) polygon = cv2.ellipse2Poly((int(mY),int(mX)), (int(length/ii), stickwidth), int(bending), 0, 360, i) cv2.fillConvexPoly(cur_canvas, polygon, (int(colour[0]), int(color[ane]), int(color[2]))) epitome = cv2.addWeighted(epitome, 0.five, cur_canvas, 0.5, 0) render prototype
Case 19
def test_image(image_path, model_path): frame = cv2.imread(image_path) h, w = frame.shape[:two] landmarks = True centerface = CenterFace(model_path=model_path, landmarks=landmarks) centerface.transform(h, west) if landmarks: dets, lms = centerface(frame, threshold=0.35) else: dets = centerface(frame, threshold=0.35) for det in dets: boxes, score = det[:4], det[4] cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[two]), int(boxes[iii])), (2, 255, 0), 1) if landmarks: for lm in lms: cv2.circle(frame, (int(lm[0]), int(lm[1])), ii, (0, 0, 255), -1) cv2.circle(frame, (int(lm[2]), int(lm[three])), ii, (0, 0, 255), -1) cv2.circle(frame, (int(lm[4]), int(lm[five])), 2, (0, 0, 255), -1) cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1) cv2.circumvolve(frame, (int(lm[eight]), int(lm[9])), 2, (0, 0, 255), -i) cv2.imshow('out', frame) cv2.waitKey(0) Example 20
def plot_kpt(image, kpt): ''' Draw 68 key points Args: epitome: the input image kpt: (68, 3). ''' epitome = image.copy() kpt = np.circular(kpt).astype(np.int32) for i in range(kpt.shape[0]): st = kpt[i, :2] image = cv2.circle(image, (st[0], st[i]), 1, (0, 0, 255), 2) if i in end_list: continue ed = kpt[i + 1, :2] image = cv2.line(image, (st[0], st[1]), (ed[0], ed[i]), (255, 255, 255), 1) return image
Example 21
def apply_keypoint(epitome, keypoint, num_joints=17): image = image.astype(np.uint8) edges = [[0, 1], [0, 2], [1, 3], [ii, iv], [3, 5], [four, 6], [5, vi], [5, 7], [7, 9], [vi, 8], [8, 10], [five, xi], [6, 12], [11, 12], [11, 13], [13, 15], [12, 14], [14, xvi]] for j in range(num_joints): if keypoint[j][2]>0.: cv2.circle(image, (keypoint[j, 0], keypoint[j, 1]), three, (255,255,255), ii) stickwidth = 2 for j, e in enumerate(edges): if keypoint[e[0],ii] > 0. and keypoint[east[1],ii] > 0.: centerA = keypoint[e[0],:2] centerB = keypoint[e[1],:2] cv2.line(epitome,(centerA[0], centerA[ane]),(centerB[0], centerB[1]),(255, 255,255),2) return image
Example 22
def chief(): """Exam code""" global mp mp = np.array((2, ane), np.float32) # measurement def onmouse(one thousand, x, y, s, p): global mp mp = np.array([[np.float32(x)], [np.float32(y)]]) cv2.namedWindow("kalman") cv2.setMouseCallback("kalman", onmouse) kalman = Stabilizer(4, ii) frame = np.zeros((480, 640, 3), np.uint8) # cartoon sheet while True: kalman.update(mp) point = kalman.prediction state = kalman.filter.statePost cv2.circumvolve(frame, (land[0], land[1]), 2, (255, 0, 0), -ane) cv2.circle(frame, (point[0], indicate[i]), ii, (0, 255, 0), -ane) cv2.imshow("kalman", frame) k = cv2.waitKey(thirty) & 0xFF if thou == 27: suspension Example 23
def __getitem__(self, idx): # traj sizeL (n_frames, n_balls, 4) traj = self.dataset[idx] vid_len, n_balls = traj.shape[:two] if self.is_train: start = random.randint(0, vid_len - self.n_frames) else: offset = 0 n_channels = ane images = np.zeros([self.n_frames, self.size, self.size, n_channels], np.uint8) positions = [] for fid in range(self.n_frames): xy = [] for bid in range(n_balls): # each ball: ball = traj[start + fid, bid] x, y = int(round(self.scale * brawl[0])), int(round(cocky.calibration * brawl[1])) images[fid] = cv2.circle(images[fid], (x, y), int(cocky.radius * ball[3]), 255, -ane) xy.suspend([x / self.size, y / self.size]) positions.suspend(xy) if self.transform is not None: images = self.transform(images) input = images[:self.n_frames_input] if self.n_frames_output > 0: output = images[self.n_frames_input:] else: output = [] if not cocky.return_positions: return input, output else: positions = np.assortment(positions) return input, output, positions
Example 24
def circle(cocky, x, y, radius, characterization=None): """Draw a circle. Parameters ---------- ten : bladder | int Center of the circle (x-axis). y : float | int Center of the circle (y-axis). radius : float | int Radius of the circle in pixels. label : Optional[str] A text label that is placed at the center of the circumvolve. """ image_size = int(radius + self.thickness + ane.5) # really half size roi = int(10 - image_size), int(y - image_size), \ int(2 * image_size), int(two * image_size) if not is_in_bounds(cocky.image, roi): return paradigm = view_roi(self.paradigm, roi) eye = image.shape[ane] // 2, paradigm.shape[0] // 2 cv2.circle( image, center, int(radius + .5), self._color, self.thickness) if label is not None: cv2.putText( self.image, label, center, cv2.FONT_HERSHEY_PLAIN, 2, cocky.text_color, 2)
Example 25
def showTransform(epitome): # pragma: no cover im = image.copy() for (cx, cy) in pts1: cv2.circle(im, (int(cx), int(cy)), 8, (0, 255, 0), -1) imshow(im, name="transform")
Example 26
def object_track(self, img, conf_th=0.3, conf_class=[]): output = self.detector.prediction(img) df = self.detector.filter_prediction(output, img, conf_th=conf_th, conf_class=conf_class) img = self.detector.draw_boxes(img, df) boxes = df[['x1', 'y1', 'x2', 'y2']].values objects = cocky.ct.update(boxes) if len(boxes) > 0 and (df['class_name'].str.contains('person').any()): for (objectID, centroid) in objects.items(): text = "ID {}".format(objectID) cv2.putText(img, text, (centroid[0] - ten, centroid[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), two) cv2.circle(img, (centroid[0], centroid[1]), four, (0, 255, 0), -i) return img Case 27
def ObjectTracking(self): detector = Detector() myiter = glob.iglob(os.path.join(IMAGE_FOLDER, '**', '*.jpg'), recursive=True) newdict = reduce(lambda a, b: reduce_tracking(a,b), myiter, dict()) startID = max(map(int, newdict.keys()), default=0) + 1 ct = CentroidTracker(startID=startID) with PiCamera() as photographic camera: camera.resolution = (1280, 960) # twice superlative and widht camera.rotation = int(str(bone.environ['CAMERA_ROTATION'])) photographic camera.framerate = 10 with PiRGBArray(camera, size=(WIDTH, Height)) as output: while True: camera.capture(output, 'bgr', resize=(WIDTH, HEIGHT)) img = output.array result = detector.prediction(img) df = detector.filter_prediction(result, img) img = detector.draw_boxes(img, df) boxes = df[['x1', 'y1', 'x2', 'y2']].values previous_object_ID = ct.nextObjectID objects = ct.update(boxes) if len(boxes) > 0 and (df['class_name'].str.contains('person').whatever()) and previous_object_ID in listing(objects.keys()): for (objectID, centroid) in objects.items(): text = "ID {}".format(objectID) cv2.putText(img, text, (centroid[0] - x, centroid[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), ii) cv2.circumvolve(img, (centroid[0], centroid[i]), four, (0, 255, 0), -1) twenty-four hour period = datetime.now().strftime("%Y%m%d") directory = bone.path.bring together(IMAGE_FOLDER, 'pi', 24-hour interval) if not os.path.exists(directory): bone.makedirs(directory) ids = "-".bring together(list([str(i) for i in objects.keys()])) hour = datetime.now().strftime("%H%K%S") filename_output = os.path.join( directory, "{}_person_{}_.jpg".format(hr, ids) ) cv2.imwrite(filename_output, img) time.slumber(0.300) Example 28
def object_track(self, img, conf_th=0.iii, conf_class=[]): output = self.detector.prediction(img) df = self.detector.filter_prediction(output, img, conf_th=conf_th, conf_class=conf_class) img = cocky.detector.draw_boxes(img, df) boxes = df[['x1', 'y1', 'x2', 'y2']].values objects = self.ct.update(boxes) if len(boxes) > 0 and (df['class_name'].str.contains('person').any()): for (objectID, centroid) in objects.items(): text = "ID {}".format(objectID) cv2.putText(img, text, (centroid[0] - ten, centroid[i] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.circle(img, (centroid[0], centroid[1]), iv, (0, 255, 0), -1) return img Example 29
def object_track(self, img, conf_th=0.3, conf_class=[]): output = cocky.detector.prediction(img) boxes, confs, clss = cocky.detector.filter_prediction(output, img, conf_th=conf_th, conf_class=conf_class) img = cocky.detector.draw_boxes(img, boxes, confs, clss) objects = self.ct.update(boxes) if len(boxes) > 0 and 1 in clss: for (objectID, centroid) in objects.items(): text = "ID {}".format(objectID) cv2.putText(img, text, (centroid[0] - ten, centroid[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.five, (0, 255, 0), 2) cv2.circle(img, (centroid[0], centroid[ane]), 4, (0, 255, 0), -1) return img Case 30
def draw_datches(img1, kp1, img2, kp2, matches, color=None, kp_radius=5, thickness=two, margin=twenty): # Create frame if len(img1.shape) == 3: new_shape = (max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[ane]+margin, img1.shape[2]) elif len(img1.shape) == two: new_shape = (max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[ane]+margin) new_img = np.ones(new_shape, type(img1.flat[0]))*255 # Identify original images new_img[0:img1.shape[0], 0:img1.shape[i]] = img1 new_img[0:img2.shape[0], img1.shape[1]+margin:img1.shape[ane]+img2.shape[one]+margin] = img2 # Draw lines betwixt matches if colour: c = color for m in matches: # Generate random color for RGB/BGR and grayscale images equally needed. if non color: if len(img1.shape) == 3: c = np.random.randint(0, 256, 3) else: c = np.random.randint(0, 256) c = (int(c[0]), int(c[1]), int(c[2])) end1 = tuple(np.round(kp1[m.trainIdx].pt).astype(int)) end2 = tuple(np.round(kp2[thou.queryIdx].pt).astype(int) + np.array([img1.shape[ane]+margin, 0])) cv2.line(new_img, end1, end2, c, thickness, lineType=cv2.LINE_AA) cv2.circumvolve(new_img, end1, kp_radius, c, thickness, lineType=cv2.LINE_AA) cv2.circle(new_img, end2, kp_radius, c, thickness, lineType=cv2.LINE_AA) return new_img
Source: https://www.programcreek.com/python/example/84097/cv2.circle
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