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How to use list_images method in tempest

Best Python code snippet using tempest_python

augmentation.py

Source:augmentation.py

1import random2import numpy as np3import cv24import torch5import SimpleITK as sitk6import numpy as np7import os8import random9import cv210import torch11# Read in all images for a patient as a dict of np arrays12# Always in shape CDHW13def read_data(patient_dir):14    dict_images = {}15    list_structures = ['CT', 'PTV70', 'PTV63', 'PTV56',16                       'possible_dose_mask', 'Brainstem', 'SpinalCord',17                       'RightParotid', 'LeftParotid', 'Esophagus',18                       'Larynx', 'Mandible', 'dose']19    for structure_name in list_structures:20        structure_file = patient_dir + '/' + structure_name + '.nii.gz'21        if structure_name == 'CT':22            dtype = sitk.sitkInt1623        elif structure_name == 'dose':24            dtype = sitk.sitkFloat3225        else:26            dtype = sitk.sitkUInt827        if os.path.exists(structure_file):28            dict_images[structure_name] = sitk.ReadImage(structure_file, dtype)29            # To numpy array (C * Z * H * W)30            dict_images[structure_name] = sitk.GetArrayFromImage(dict_images[structure_name])[np.newaxis, :, :, :]31        else:32            dict_images[structure_name] = np.zeros((1, 128, 128, 128), np.uint8)33    return dict_images34def preprocess_image(dict_images):35    # PTVs36    PTVs = 70.0 / 70. * dict_images['PTV70'] \37           + 63.0 / 70. * dict_images['PTV63'] \38           + 56.0 / 70. * dict_images['PTV56']39    # OARs40    OAR_names = ['Brainstem', 'SpinalCord', 'RightParotid', 'LeftParotid',41                 'Esophagus', 'Larynx', 'Mandible']42    OAR_all = np.concatenate([dict_images[OAR] for OAR in OAR_names], axis=0)43    # CT image44    CT = dict_images['CT']45    CT = np.clip(CT, a_min=-1024, a_max=1500)46    CT = CT.astype(np.float32) / 1000.47    # Dose image48    dose = dict_images['dose'] / 70.49    # Possible_dose_mask, the region that can receive dose50    possible_dose_mask = dict_images['possible_dose_mask']51    list_images = [np.concatenate((PTVs, OAR_all, CT), axis=0),  # Input52                   dose,  # Label53                   possible_dose_mask]54    return list_images55def random_flip_3d(list_images, list_axis=(0, 1, 2), p=0.5):56    if random.random() <= p:57        if 0 in list_axis:58            if random.random() <= 0.5:59                for image_i in range(len(list_images)):60                    list_images[image_i] = list_images[image_i][:, ::-1, :, :].copy()61        if 1 in list_axis:62            if random.random() <= 0.5:63                for image_i in range(len(list_images)):64                    list_images[image_i] = list_images[image_i][:, :, ::-1, :].copy()65        if 2 in list_axis:66            if random.random() <= 0.5:67                for image_i in range(len(list_images)):68                    list_images[image_i] = list_images[image_i][:, :, :, ::-1].copy()69    return list_images70# Random rotation using OpenCV71def random_rotate_around_z_axis(list_images,72                                list_angles,73                                list_interp,74                                list_boder_value,75                                p=0.5):76    if random.random() <= p:77        # Randomly pick an angle list_angles78        _angle = random.sample(list_angles, 1)[0]79        # Do not use random scaling, set scale factor to 180        _scale = 1.81        for image_i in range(len(list_images)):82            for chan_i in range(list_images[image_i].shape[0]):83                for slice_i in range(list_images[image_i].shape[1]):84                    rows, cols = list_images[image_i][chan_i, slice_i, :, :].shape85                    M = cv2.getRotationMatrix2D(((cols - 1) / 2.0, (rows - 1) / 2.0), _angle, scale=_scale)86                    list_images[image_i][chan_i, slice_i, :, :] = \87                        cv2.warpAffine(list_images[image_i][chan_i, slice_i, :, :],88                                       M,89                                       (cols, rows),90                                       borderMode=cv2.BORDER_CONSTANT,91                                       borderValue=list_boder_value[image_i],92                                       flags=list_interp[image_i])93    return list_images94# Random translation95def random_translate(list_images, roi_mask, p, max_shift, list_pad_value):96    if random.random() <= p:97        exist_mask = np.where(roi_mask > 0)98        ori_z, ori_h, ori_w = list_images[0].shape[1:]99        bz = min(max_shift - 1, np.min(exist_mask[0]))100        ez = max(ori_z - 1 - max_shift, np.max(exist_mask[0]))101        bh = min(max_shift - 1, np.min(exist_mask[1]))102        eh = max(ori_h - 1 - max_shift, np.max(exist_mask[1]))103        bw = min(max_shift - 1, np.min(exist_mask[2]))104        ew = max(ori_w - 1 - max_shift, np.max(exist_mask[2]))105        for image_i in range(len(list_images)):106            list_images[image_i] = list_images[image_i][:, bz:ez + 1, bh:eh + 1, bw:ew + 1]107        # Pad to original size108        list_images = random_pad_to_size_3d(list_images,109                                            target_size=[ori_z, ori_h, ori_w],110                                            list_pad_value=list_pad_value)111    return list_images112# To tensor, images should be C*Z*H*W113def to_tensor(list_images):114    for image_i in range(len(list_images)):115        list_images[image_i] = torch.from_numpy(list_images[image_i].copy()).float()116    return list_images117# Pad118def random_pad_to_size_3d(list_images, target_size, list_pad_value):119    _, ori_z, ori_h, ori_w = list_images[0].shape[:]120    new_z, new_h, new_w = target_size[:]121    pad_z = new_z - ori_z122    pad_h = new_h - ori_h123    pad_w = new_w - ori_w124    pad_z_1 = random.randint(0, pad_z)125    pad_h_1 = random.randint(0, pad_h)126    pad_w_1 = random.randint(0, pad_w)127    pad_z_2 = pad_z - pad_z_1128    pad_h_2 = pad_h - pad_h_1129    pad_w_2 = pad_w - pad_w_1130    output = []131    for image_i in range(len(list_images)):132        _image = list_images[image_i]133        output.append(np.pad(_image,134                             ((0, 0), (pad_z_1, pad_z_2), (pad_h_1, pad_h_2), (pad_w_1, pad_w_2)),135                             mode='constant',136                             constant_values=list_pad_value[image_i])137                      )...

cs231_filters.py

Source:cs231_filters.py

1import numpy as np2import cv23import matplotlib.pyplot as plt4from skimage import exposure5def Sharp_img(img):6	kernel3 = np.array([[-1, -1,  -1],7        		    [-1,  9, -1],8                	    [-1, -1,  -1]])9	res = cv2.filter2D(src=img, ddepth=-1, kernel=kernel3)10	return res11def Gray_img(img):12	res = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)13	return res14def Bw_img(img, t ):15	img_gray_sharp = cv2.cvtColor(Sharp_img(img), cv2.COLOR_BGR2GRAY)16	max_value = np.max( img_gray_sharp )17	t = t * max_value / 25518	#t = np.average(img_gray_sharp) - 2519	thresh, res = cv2.threshold(img_gray_sharp,t,255,cv2.THRESH_BINARY)20	return res21def Equal_img(img, grid = None):22	b, g, r = cv2.split(img)23	if grid is not None:24		clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(grid, grid))25	else:26		clahe = cv2.createCLAHE(clipLimit=3.0)27	clahe_b = clahe.apply(b)28	clahe_g = clahe.apply(g)29	clahe_r = clahe.apply(r) 30	31	res = cv2.merge((clahe_b, clahe_g, clahe_r))32	return res33def Gray_equal_img(img):34	img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)35	clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))36	res = clahe.apply(img_gray)37	return res38def Contrast_stretching_img(img ):39	p2, p98 = np.percentile(img, (2, 98))40	res = exposure.rescale_intensity(img, in_range=(p2, p98))41	return res42def Gray_Contrast_stretching_img(img ):43	img = cv2.cvtColor(Sharp_img(img), cv2.COLOR_BGR2GRAY)44	p2, p98 = np.percentile(img, (2, 98))45	res = exposure.rescale_intensity(img, in_range=(p2, p98))46	return res47######################################################48################# TESTING49'''	50List_images = []51# Original Image52_img = cv2.imread("IMG_5530.jpg")53List_images.append(_img)54# Origin -> Sharpening Image55img_sharp = Sharp_img(_img)56List_images.append(img_sharp)57# Origin -> Grayscale Image58img_gray = Gray_img(_img)59List_images.append(img_gray)60# Sharp -> B&W Image61img_bw = Bw_img(_img)62List_images.append(img_bw)63# Origin -> Equalization Image64img_equal = Equal_img(_img)65List_images.append(img_equal)66# Origin -> Equalizing Grayscale Image67img_equal_gray = Gray_equal_img(_img)68List_images.append(img_equal_gray)69# Origin -> Contrast Stretching Image70img_rescale = Contrast_stretching_img(_img)71List_images.append(img_rescale)72# Sharp -> Contrast Stretching Image73img_rescale = Contrast_stretching_img(_img,'sharp')74List_images.append(img_rescale)75# Original -> Contrast Stretching Grayscale Image76img_rescale = Contrast_stretching_img(_img,'gray')77List_images.append(img_rescale)78# Sharp -> Contrast Stretching Grayscale Image79img_rescale = Contrast_stretching_img(_img,'graysharp')80List_images.append(img_rescale)81plt.figure(num="Image Fig" ,figsize=(100,100))82title = ["Origin","Sharp Image","Grayscale","B&W","Equalizing","Grayscale Equalizing"83		,"Contrast","Sharp Contrast","Grayscale Contrast"84		,"Grayscale Sharp Contrast"]85idx = 186for i, t in zip(List_images,title):87	img = cv2.cvtColor(i, cv2.COLOR_BGR2RGB)88	plt.subplot(2, 5, idx)89	plt.title(t)90	plt.axis('off')91	plt.imshow(img,cmap='gray')92	idx+=193plt.show()...

testing_code.py

Source:testing_code.py

1import numpy as np2import cv23import matplotlib.pyplot as plt4from skimage import exposure5List_images = []6# Original Image7_img = cv2.imread("test.jpg")8List_images.append(_img)9# Origin -> Sharpening Image10kernel3 = np.array([[0, -1,  0],11                   [-1,  5, -1],12                    [0, -1,  0]])13img_sharp = cv2.filter2D(src=_img, ddepth=-1, kernel=kernel3)14List_images.append(img_sharp)15# Origin -> Grayscale Image16img_gray = cv2.cvtColor(_img, cv2.COLOR_BGR2GRAY)17List_images.append(img_gray)18# Sharp -> B&W Image19img_gray_sharp = cv2.cvtColor(img_sharp, cv2.COLOR_BGR2GRAY)20t = np.average(img_gray_sharp) - 2521thresh, img_bw = cv2.threshold(img_gray_sharp,t,255,cv2.THRESH_BINARY)22List_images.append(img_bw)23# Origin -> Equalization Image24def Adapt_equal(img, grid = None):25  b, g, r = cv2.split(img)26  if grid is not None:27    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(grid, grid))28  else:29    clahe = cv2.createCLAHE(clipLimit=3.0)30  clahe_b = clahe.apply(b)31  clahe_g = clahe.apply(g)32  clahe_r = clahe.apply(r) 33  equa_img = cv2.merge((clahe_b, clahe_g, clahe_r))34  return equa_img35img_equal = Adapt_equal(_img,8)36List_images.append(img_equal)37# Origin -> Equalizing Grayscale Image38clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))39img_equal_gray = clahe.apply(img_gray)40List_images.append(img_equal_gray)41#img_equal_gray_s = clahe.apply(img_gray_sharp)42# Origin -> Contrast Stretching Image43p2, p98 = np.percentile(_img, (2, 98))44img_rescale = exposure.rescale_intensity(_img, in_range=(p2, p98))45List_images.append(img_rescale)46# Sharp -> Contrast Stretching Image47p2, p98 = np.percentile(_img, (2, 98))48img_rescale_sharp = exposure.rescale_intensity(img_sharp, in_range=(p2, p98))49List_images.append(img_rescale_sharp)50# Original -> Contrast Stretching Grayscale Image51p2, p98 = np.percentile(img_gray, (2, 98))52img_rescale_gray = exposure.rescale_intensity(img_gray, in_range=(p2, p98))53List_images.append(img_rescale_gray)54# Sharp -> Contrast Stretching Grayscale Image55p2, p98 = np.percentile(img_gray_sharp, (2, 98))56img_rescale_graysharp = exposure.rescale_intensity(img_gray_sharp, in_range=(p2, p98))57List_images.append(img_rescale_graysharp)58""" for i in List_images:59	cv2.resizeWindow("output", 200, 300)     60	cv2.imshow("",i)61	cv2.waitKey(0) """62plt.figure(num="Image Fig" ,figsize=(100,100))63title = ["Origin","Sharp Image","Grayscale","B&W","Equalizing","Grayscale Equalizing"64		,"Contrast","Sharp Contrast","Grayscale Contrast"65		,"Grayscale Sharp Contrast"]66idx = 167for i, t in zip(List_images,title):68	img = cv2.cvtColor(i, cv2.COLOR_BGR2RGB)69	plt.subplot(2, 5, idx)70	plt.title(t)71	plt.axis('off')72	plt.imshow(img,cmap='gray')73	idx+=1...

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