np.random.randint(0, 256) g = np.random.randint(0, 256) r = np.random.randint(0, 256) # cv.line(image, (np.int...(x1), np.int(y1)), (np.int(x2), np.int(y2)), (b, g, r), 4, cv.LINE_8, 0) cv.rectangle(image, (np.int...(x1), np.int(y1)), (np.int(x2), np.int(y2)), (b, g, r), 1, cv.LINE_8, 0) cv.imshow("image", image
_.dtype) # print(f.dtype) ## AttributeError: ‘int’ object has no attribute ‘dtype’ # print(a.astype(np.int...)) ## AttributeError: ‘list’ object has no attribute ‘astype’ # print(b.astype(np.int)) ## AttributeError...: ‘dict’ object has no attribute ‘astype’ print(c.astype(np.int)) # print(d.astype(np.int)) ## AttributeError...: ‘Myclass’ object has no attribute ‘astype’ print(e.astype(np.int)) # print(f.astype(np.int)) ## AttributeError
dtype) # print(f.dtype) ## AttributeError: 'int' object has no attribute 'dtype' # print(a.astype(np.int...)) ## AttributeError: 'list' object has no attribute 'astype' # print(b.astype(np.int)) ## AttributeError...: 'dict' object has no attribute 'astype' print(c.astype(np.int)) # print(d.astype(np.int)) ## AttributeError...: 'Myclass' object has no attribute 'astype' print(e.astype(np.int)) # print(f.astype(np.int)) ## AttributeError
import cv2 import numpy as np import math def transform1(img): rows,cols,c=img.shape R=np.int...theta=math.pi/2+tan_inv else: theta=math.pi*3/2+tan_inv xp=np.int...(np.floor(theta/2/math.pi*cols)) yp=np.int(np.floor(r/R*rows)) new_img[j,i]=img...[rows-yp-1,xp] return new_img def transform2(img): rows,cols,c=img.shape R=np.int(cols/2/...(np.floor(theta/2/math.pi*cols)) yp=np.int(np.floor(r/R*rows)-1) new_img[j,i]
, col, 0] g = image[row, col, 1] r = image[row, col, 2] index = np.int...(b/hsize)*16*16 + np.int(g/hsize)*16 + np.int(r/hsize) rgHist[np.int(index), 0] = rgHist[...np.int(index), 0] + 1 return rgHist def hist_compare(image1, image2): hist1 = create_rgb_hist
enumerate(face_landmarks.landmark): if idx == 168 or idx == 197: # middle x1 = np.int...y1), 4, (0, 0, 255), 4, cv2.LINE_AA) if idx == 162 or idx == 111: # left x1 = np.int..., 4, (255, 0, 255), 4, cv2.LINE_AA) if idx == 445 or idx == 448: # right x1 = np.int...(right_blob) outs = net.forward() pts = np.reshape(outs, (-1, 2)) for pt in pts: x = np.int...(pt[0] * rw) y = np.int(pt[1] * rh) cv2.circle(right_roi, (x, y), 1, (0, 0, 255), 0) 运行结果如下:
params) keypoints = detector.detect(binary) blob_info = [] for kp in keypoints: cv.circle(image, (np.int...(kp.pt[0]), np.int(kp.pt[1])), 3, (0, 255, 0), -1, 8) cv.circle(image, (np.int(kp.pt[0]), np.int(...kp.pt[1])), np.int(kp.size/2), (0, 0, 255), 2, 8) cv.imwrite("D:/result.png", image) cv.waitKe
low (inclusive) to high (exclusive) . random_integers(low[, high, size]) Random integers of type np.int...list_randint [[16 14 13 16 17]] random_integers(low[, high, size]): Random integers of type np.int...类型为np.int的随机整数,包括低和高。...import numpy as np # random_integers(low[, high, size]) Random integers of type np.int between low...and high, inclusive. # random_integers(low[, high, size]) 类型为np.int的随机整数,包括低和高。
= tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int...test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int...load_csv_with_header() 有三个参数 - filename, 数据地址 - target_dtype, 目标值的numpy datatype(iris的目标值是0,1,2,所以是np.int...= tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int...test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int
print('\nnp.zeros(5)生成的array=\n{}'.format(np.ones(5))) print('\nnp.zeros((5,),dtype=np.int)生成的array=\...n{}'.format(np.zeros((5,),dtype=np.int))) print('\nnp.zeros((2,1))生成的array=\n{}'.format(np.zeros((2,1...array=\n{}'.format(np.zeros_like(y))) np.zeros(5)生成的array= [ 1. 1. 1. 1. 1.] np.zeros((5,),dtype=np.int...相应用法同5.zeros print('\nnp.ones(4)生成的array=\n{}'.format(np.ones(4))) print('\nnp.ones((4,),dtype=np.int...)生成的array=\n{}'.format(np.ones((4,),dtype=np.int))) print('\nnp.ones((2,1))生成的array=\n{}'.format(np.ones
print('\nnp.zeros(5)生成的array=\n{}'.format(np.ones(5))) print('\nnp.zeros((5,),dtype=np.int)生成的array=...\n{}'.format(np.zeros((5,),dtype=np.int))) print('\nnp.zeros((2,1))生成的array=\n{}'.format(np.zeros((2,1...np.zeros((5,),dtype=np.int)生成的array= [0 0 0 0 0] np.zeros((2,1))生成的array= [[ 0.] [ 0.]]...相应用法同5.zeros print('\nnp.ones(4)生成的array=\n{}'.format(np.ones(4))) print('\nnp.ones((4,),dtype=np.int...np.ones((4,),dtype=np.int)生成的array= [1 1 1 1] np.ones((2,1))生成的array= [[ 1.] [ 1.]]
/test_cp.avi", cv.VideoWriter_fourcc('D', 'I', 'V', 'X'), 15, (np.int(width), np.int
方案一:重新安装numpy(不推荐,修改版本号可能会引发其他代码错误) pip uninstall numpy pip install numpy==1.22 方案二:改代码 找到报错地方,将 np.int...修改为 np.int_ 以我的代码举例: self.sf = np.int(data['sf'][0,...].squeeze().cpu().numpy()) 修改为: self.sf = np.int
(image, (x, y), (x + w, y + h), (255, 0, 0), 1) cx = w / 2 cy = h / 2 cv.circle(image, (np.int...(x + cx), np.int(y + cy)), 1, (255, 0, 0)) ## 在图上标出圆心 center = [np.int(x + cx), np.int(y + cy)]...(cx), np.int(cy)), 3, (255), -1) cv.rectangle(self.src, (x, y), (x + w, y + h), (255, 0, 0), 1) cx =...w / 2 cy = h / 2 cv.circle(self.src, (np.int(x + cx), np.int(y + cy)), 1, (255, 0, 0)) center.extend(...[np.int(x + cx), np.int(y + cy)]) break cv.imshow('center', self.src) return center def extract(self,
(image): h, w = image.shape[:2] nums = 10000 rows = np.random.randint(0, h, nums, dtype=np.int...) cols = np.random.randint(0, w, nums, dtype=np.int) for i in range(nums): if i % 2 =
= tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int...test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int...= tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int...test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int
. - lam) cut_w = np.int(W * cut_rat) cut_h = np.int(H * cut_rat) # uniform cx = np.random.randint
(anchor_pts[3] - anchor_pts[1]) dst_mask = cv.resize(mask, (0, 0), fx=rate_x, fy=rate_y) start_x = np.int...(anchor_pts[0] * rate_x); start_y = np.int(anchor_pts[1] * rate_y); end_x = np.int(anchor_pts[2] * rate_x...) end_y = np.int(anchor_pts[3] * rate_y) # 贴图 for row in range(end_y - start_y): for col in range
领取专属 10元无门槛券
手把手带您无忧上云