当我希望出现索引时如何使用numpy.hist

第4张图片 第二张4张图片 第4张图片 后4张图片 我的结果必须正确 贝里尔结果 解决方案您可以这样做: import cv2import numpy as npimport matplotlib.pyplot as plt# load imageimg = cv2.imread('lenna.png')# convert from BGR to HSVimg_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# get the Hue channelhue = img_hsv[:, :, 0]# show histogramhist, bin_edges = np.histogram(hue, bins=range(180))plt.bar(bin_edges[:-1], hist)plt.show() 如果您不需要直方图值，则可以通过以下方式进行操作: import cv2import matplotlib.pyplot as plt# load imageimg = cv2.imread('lenna.png')# convert from BGR to HSVimg_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# get the Hue channelhue = img_hsv[:, :, 0]# show histogramplt.hist(hue.flatten(), bins=range(180))plt.show() 输入(lenna.png): 输出: 如果您有多张图片，则可以执行以下操作:import cv2import numpy as npimport matplotlib.pyplot as plt# list of paths to the imagesimage_fname_list = ['lenna.png', 'other_image.png', ...]# var to accumulate the histogramstotal_hue_hist = np.zeros((179,))for image_fname in image_fname_list: # load image img = cv2.imread(image_fname) # convert from BGR to HSV img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # get the Hue channel hue = img_hsv[:, :, 0] # show histogram hist, bin_edges = np.histogram(hue, bins=range(180)) total_hue_hist += histplt.bar(list(range(179)), hist)plt.show()I have a set of images and i want to create a histogram over the hue values from each image. Therfore i created an array with length 180. In every cell i add 1 if the hue value is in the image. In the end i have the array with the occurence of each hue value, but when i use numpy.hist, the y-axis are the hue values and the x-axis are the occurence. But i want it the other way round. Here is my code:path = 'path'sub_path = 'subpath'sumHueOcc = np.zeros((180, 1), dtype=int) print("sumHue Shape")print(sumHueOcc.shape)for item in dirs: fullpath = os.path.join(path,item) pathos = os.path.join(sub_path,item) if os.path.isfile(fullpath): img = np.array(Image.open(fullpath)) f, e = os.path.splitext(pathos) imgHSV = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) print("Img shape") print(img.shape) # want to work with hue only h, s, v = cv2.split(imgHSV) # the hue values in one large array Z = h.reshape((-1, 1)) # convert to np.float32 Z = np.uint32(Z) # add 1 for each hue value in the image for z in Z: sumHueOcc[z] = sumHueOcc[z] + 1 plt.figure(figsize=(9, 8)) plt.subplot(311) # Hue Picture 1 plt.subplots_adjust(hspace=.5) plt.title("Hue Picture 1") plt.hist(np.ndarray.flatten(h), bins=180) plt.subplot(312) # Hue Picture 2 plt.subplots_adjust(hspace=.5) plt.title("Hue Picture 2") plt.hist(np.ndarray.flatten(Z), bins=180) plt.subplot(313) # Hue Picture 2 plt.subplots_adjust(hspace=.5) plt.title("Sum Occ") plt.hist(np.ndarray.flatten(sumHueOcc), bins=180) plt.show()#First Hue Sumplt.figure(figsize=(9,8))plt.title("Sum Hue Occ")plt.hist(np.ndarray.flatten(sumHueOcc), bins=180)plt.show()Here is Berriels Code with the change from Half Hue to Full Hue:print(glob.glob('path with my 4 images'))# list of paths to the imagesimage_fname_list = glob.glob('path with my 4 images')# var to accumulate the histogramstotal_hue_hist = np.zeros((359,))for image_fname in image_fname_list: # load image img = cv2.imread(image_fname) # convert from BGR to HSV img = np.float32(img) img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL) # get the Hue channel #hue = img_hsv[:, :, 0] hue, sat, val = cv2.split(img_hsv) # show histogram hist, bin_edges = np.histogram(hue, bins=range(360)) total_hue_hist += histplt.bar(list(range(359)), hist)plt.show()Sum Occ has to be the same as Hue Picture 1 and 2First of 4 PicturesSecond of 4 PicturesThird of 4 PicturesLast of 4 PicturesMy result, which has to be correctBerriels result 解决方案 You can do this way:import cv2import numpy as npimport matplotlib.pyplot as plt# load imageimg = cv2.imread('lenna.png')# convert from BGR to HSVimg_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# get the Hue channelhue = img_hsv[:, :, 0]# show histogramhist, bin_edges = np.histogram(hue, bins=range(180))plt.bar(bin_edges[:-1], hist)plt.show()If you don't need to histogram values, you can do this way:import cv2import matplotlib.pyplot as plt# load imageimg = cv2.imread('lenna.png')# convert from BGR to HSVimg_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# get the Hue channelhue = img_hsv[:, :, 0]# show histogramplt.hist(hue.flatten(), bins=range(180))plt.show()Input (lenna.png):Output:If you have multiple images, you can do something like this:import cv2import numpy as npimport matplotlib.pyplot as plt# list of paths to the imagesimage_fname_list = ['lenna.png', 'other_image.png', ...]# var to accumulate the histogramstotal_hue_hist = np.zeros((179,))for image_fname in image_fname_list: # load image img = cv2.imread(image_fname) # convert from BGR to HSV img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # get the Hue channel hue = img_hsv[:, :, 0] # show histogram hist, bin_edges = np.histogram(hue, bins=range(180)) total_hue_hist += histplt.bar(list(range(179)), hist)plt.show() 这篇关于当我希望出现索引时如何使用numpy.hist的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！