Python cv2.imwrite方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def main():
	imagePath = "img.jpg"
	
	img = cv2.imread(imagePath)
	gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
	
	generate_histogram(gray)
	
	cv2.imwrite("before.jpg", gray)

	gray = cv2.equalizeHist(gray)
	
	generate_histogram(gray)
	
	cv2.imwrite("after.jpg",gray)
	
	return 0 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def _lapulaseDetection(self, imgName):
        """
        :param strdir: 文件所在的目录
        :param name: 文件名称
        :return: 检测模糊后的分数
        """
        # step1: 预处理
        img2gray, reImg = self.preImgOps(imgName)
        # step2: laplacian算子 获取评分
        resLap = cv2.Laplacian(img2gray, cv2.CV_64F)
        score = resLap.var()
        print("Laplacian %s score of given image is %s", str(score))
        # strp3: 绘制图片并保存  不应该写在这里  抽象出来   这是共有的部分
        newImg = self._drawImgFonts(reImg, str(score))
        newDir = self.strDir + "/_lapulaseDetection_/"
        if not os.path.exists(newDir):
            os.makedirs(newDir)
        newPath = newDir + imgName
        # 显示
        cv2.imwrite(newPath, newImg)  # 保存图片
        cv2.imshow(imgName, newImg)
        cv2.waitKey(0)

        # step3: 返回分数
        return score 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def saveimageWithMask(img, outname, mask_poly):

    dstimg = copy.deepcopy(img)
    for mask in mask_poly:
        bound = mask.bounds
        if (len(bound) < 4):
            continue
        xmin, ymin, xmax, ymax = bound[0], bound[1], bound[2], bound[3]
        for x in range(int(xmin), int(xmax)):
            for y in range(int(ymin), int(ymax)):
                point = shgeo.Point(x, y)
                if point.within(mask):
                    #print('withing')

                    dstimg[int(y)][int(x)] = 0

    cv2.imwrite(outname, dstimg) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def test(test_loader, model, logger=None, Writer=None):
    
    model.eval()
    with torch.no_grad():
        for its, (img_line, img_noise) in enumerate(test_loader):
            img_line = img_line.cuda() if torch.cuda.is_available() else img_line
            img_noise = img_noise.cuda() if torch.cuda.is_available() else img_noise
            g_results = model(torch.cat((img_line, img_noise), 1))
            for i in range(img_line.shape[0]):
                img_line_test = img_line[i].cpu().numpy().transpose((1,2,0)) * 255
                img_line_test = img_line_test.squeeze()
                cv2.imwrite((cfg.PATH.RES_TEST+"line_{}.jpg".format(i+its)), img_line_test)

                img_res_test = g_results[i].cpu().numpy().transpose((1,2,0)) * 255
                cv2.imwrite((cfg.PATH.RES_TEST+"res_{}.jpg".format(i+its)), img_res_test)
                print("{}/{}".format(i+its,its_num)) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def convert_images2bmp():
    # cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s
    for path in ['../coco/images/val2014/', '../coco/images/train2014/']:
        folder = os.sep + Path(path).name
        output = path.replace(folder, folder + 'bmp')
        if os.path.exists(output):
            shutil.rmtree(output)  # delete output folder
        os.makedirs(output)  # make new output folder

        for f in tqdm(glob.glob('%s*.jpg' % path)):
            save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp')
            cv2.imwrite(save_name, cv2.imread(f))

    for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
        with open(label_path, 'r') as file:
            lines = file.read()
        lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace(
            '/Users/glennjocher/PycharmProjects/', '../')
        with open(label_path.replace('5k', '5k_bmp'), 'w') as file:
            file.write(lines) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def visual(title, X, activation):
    '''create a grid of images and save it as a final image
    title : grid image name
    X : array of images
    '''
    assert len(X.shape) == 4

    X = X.transpose((0, 2, 3, 1))
    if activation == 'sigmoid':
        X = np.clip((X)*(255.0), 0, 255).astype(np.uint8)
    elif activation == 'tanh':
        X = np.clip((X+1.0)*(255.0/2.0), 0, 255).astype(np.uint8)
    n = np.ceil(np.sqrt(X.shape[0]))
    buff = np.zeros((int(n*X.shape[1]), int(n*X.shape[2]), int(X.shape[3])), dtype=np.uint8)
    for i, img in enumerate(X):
        fill_buf(buff, i, img, X.shape[1:3])
    cv2.imwrite('%s.jpg' % (title), buff) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def crop_images_random(path='../images/', scale=0.50):  # from utils.utils import *; crop_images_random()
    # crops images into random squares up to scale fraction
    # WARNING: overwrites images!
    for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
        img = cv2.imread(file)  # BGR
        if img is not None:
            h, w = img.shape[:2]

            # create random mask
            a = 30  # minimum size (pixels)
            mask_h = random.randint(a, int(max(a, h * scale)))  # mask height
            mask_w = mask_h  # mask width

            # box
            xmin = max(0, random.randint(0, w) - mask_w // 2)
            ymin = max(0, random.randint(0, h) - mask_h // 2)
            xmax = min(w, xmin + mask_w)
            ymax = min(h, ymin + mask_h)

            # apply random color mask
            cv2.imwrite(file, img[ymin:ymax, xmin:xmax]) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def on_epoch_end(self, epoch, logs=None):
        if self.tiny:
            anchors = yolo_tiny_anchors
            masks = yolo_tiny_anchor_masks
        else:
            anchors = yolo_anchors
            masks = yolo_anchor_masks
        model = make_eval_model_from_trained_model(self.model, anchors, masks)

        epoch_dir = os.path.join(self.result_dir, str(epoch))

        os.makedirs(epoch_dir)
        for batch, (images, labels) in enumerate(self.dataset):
            images = images.numpy()
            for i in range(images.shape[0]):
                boxes, scores, classes = model.predict(images[i:i + 1, ...])
                img_for_this = (images[i, ...] * 255).astype(np.uint8)

                boxes_for_this, scores_for_this, classes_for_this = boxes[0, ...], scores[0, ...], classes[0, ...]

                img_for_this = draw_outputs(img_for_this, (boxes_for_this, scores_for_this, classes_for_this))
                cv2.imwrite(os.path.join(epoch_dir, '{0}.jpg'.format(uuid.uuid4())), img_for_this)
            if batch == self.num_batches:
                break 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def _blurDetection(self, imgName):

        # step 1 图像的预处理
        img2gray, reImg = self.preImgOps(imgName)
        imgMat=self._imageToMatrix(img2gray)/255.0
        x, y = imgMat.shape
        score = 0
        for i in range(x - 2):
            for j in range(y - 2):
                score += (imgMat[i + 2, j] - imgMat[i, j]) ** 2
        # step3: 绘制图片并保存  不应该写在这里  抽象出来   这是共有的部分
        score=score/10
        newImg = self._drawImgFonts(reImg, str(score))
        newDir = self.strDir + "/_blurDetection_/"
        if not os.path.exists(newDir):
            os.makedirs(newDir)
        newPath = newDir + imgName
        cv2.imwrite(newPath, newImg)  # 保存图片
        cv2.imshow(imgName, newImg)
        cv2.waitKey(0)
        return score 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def _SMDDetection(self, imgName):

        # step 1 图像的预处理
        img2gray, reImg = self.preImgOps(imgName)
        f=self._imageToMatrix(img2gray)/255.0
        x, y = f.shape
        score = 0
        for i in range(x - 1):
            for j in range(y - 1):
                score += np.abs(f[i+1,j]-f[i,j])+np.abs(f[i,j]-f[i+1,j])
        # strp3: 绘制图片并保存  不应该写在这里  抽象出来   这是共有的部分
        score=score/100
        newImg = self._drawImgFonts(reImg, str(score))
        newDir = self.strDir + "/_SMDDetection_/"
        if not os.path.exists(newDir):
            os.makedirs(newDir)
        newPath = newDir + imgName
        cv2.imwrite(newPath, newImg)  # 保存图片
        cv2.imshow(imgName, newImg)
        cv2.waitKey(0)
        return score 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def _SMD2Detection(self, imgName):
        """
        灰度方差乘积
        :param imgName:
        :return:
        """
        # step 1 图像的预处理
        img2gray, reImg = self.preImgOps(imgName)
        f=self._imageToMatrix(img2gray)/255.0
        x, y = f.shape
        score = 0
        for i in range(x - 1):
            for j in range(y - 1):
                score += np.abs(f[i+1,j]-f[i,j])*np.abs(f[i,j]-f[i,j+1])
        # strp3: 绘制图片并保存  不应该写在这里  抽象出来   这是共有的部分
        score=score
        newImg = self._drawImgFonts(reImg, str(score))
        newDir = self.strDir + "/_SMD2Detection_/"
        if not os.path.exists(newDir):
            os.makedirs(newDir)
        newPath = newDir + imgName
        cv2.imwrite(newPath, newImg)  # 保存图片
        cv2.imshow(imgName, newImg)
        cv2.waitKey(0)
        return score 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def _Variance(self, imgName):
        """
               灰度方差乘积
               :param imgName:
               :return:
               """
        # step 1 图像的预处理
        img2gray, reImg = self.preImgOps(imgName)
        f = self._imageToMatrix(img2gray)

        # strp3: 绘制图片并保存  不应该写在这里  抽象出来   这是共有的部分
        score = np.var(f)
        newImg = self._drawImgFonts(reImg, str(score))
        newDir = self.strDir + "/_Variance_/"
        if not os.path.exists(newDir):
            os.makedirs(newDir)
        newPath = newDir + imgName
        cv2.imwrite(newPath, newImg)  # 保存图片
        cv2.imshow(imgName, newImg)
        cv2.waitKey(0)
        return score 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def make_edge_smooth(dataset_name, img_size) :
    check_folder('./dataset/{}/{}'.format(dataset_name, 'trainB_smooth'))

    file_list = glob('./dataset/{}/{}/*.*'.format(dataset_name, 'trainB'))
    save_dir = './dataset/{}/trainB_smooth'.format(dataset_name)

    kernel_size = 5
    kernel = np.ones((kernel_size, kernel_size), np.uint8)
    gauss = cv2.getGaussianKernel(kernel_size, 0)
    gauss = gauss * gauss.transpose(1, 0)

    for f in tqdm(file_list) :
        file_name = os.path.basename(f)

        bgr_img = cv2.imread(f)
        gray_img = cv2.imread(f, 0)

        bgr_img = cv2.resize(bgr_img, (img_size, img_size))
        pad_img = np.pad(bgr_img, ((2, 2), (2, 2), (0, 0)), mode='reflect')
        gray_img = cv2.resize(gray_img, (img_size, img_size))

        edges = cv2.Canny(gray_img, 100, 200)
        dilation = cv2.dilate(edges, kernel)

        gauss_img = np.copy(bgr_img)
        idx = np.where(dilation != 0)
        for i in range(np.sum(dilation != 0)):
            gauss_img[idx[0][i], idx[1][i], 0] = np.sum(
                np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 0], gauss))
            gauss_img[idx[0][i], idx[1][i], 1] = np.sum(
                np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 1], gauss))
            gauss_img[idx[0][i], idx[1][i], 2] = np.sum(
                np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 2], gauss))

        cv2.imwrite(os.path.join(save_dir, file_name), gauss_img) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def loop2(self,text,w=1280,h=720):
        cap = cv2.VideoCapture(int(text))
        cap.set(6 ,cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') );
        global capnum2
        capnum2 = int(text)
        cap.set(3,w);
        cap.set(4,h);
        global update2
        update2 = 1
        global shotmark2

        while (update2 == 1):
            ret, frame = cap.read() 
            if shotmark2 == 1:
                fn = self.lineEdit.text()
                name = "photo/2_"+fn + "video.jpg"
                if os.path.exists(name):
                    name = "photo/2_" + fn + "video"+str(int(time.time()))+".jpg"
                cv2.imwrite(name, frame)
                shotmark2 = 0
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            self.original2_image.updateImage(frame)
        # cap.release()
        cv_img_rgb = np.zeros((700,700,3))
        self.original2_image.updateImage(cv_img_rgb) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import imwrite [as 别名]
def degrade_images_in_folder(
    folder,
    dst_folder_suffix,
    LIGHTDOWN=True,
    UNBALANCECOLOR=True,):
  import os
  js = os.listdir(folder)
  dst_folder = folder + '-' + dst_folder_suffix
  try:
    os.mkdir(dst_folder)
  except:
    print('dir exist!')
  print('in ' + dst_folder)
  num = 3
  for j in js:
    img = cv2.imread(folder + '/' + j) / 255.
    if LIGHTDOWN:
      for _ in range(num - 1):
        out = pow(img, np.random.uniform(0.4, 0.6)) * np.random.uniform(
            0.25, 0.5)
        cv2.imwrite(dst_folder + '/' + ('L%d-' % _) + j, out * 255.)
      out = img * img
      out = out * (1.0 / out.max())
      cv2.imwrite(dst_folder + '/' + ('L%d-' % num) + j, out * 255.)
    if UNBALANCECOLOR:
      filter = WB2()
      outs = np.array([img] * num)
      features = np.abs(np.random.rand(num, 3))
      for _, out in enumerate(
          filter.process(outs, filter.filter_param_regressor(features))):
        # print out.max()
        out /= out.max()
        out *= np.random.uniform(0.7, 1)
        cv2.imwrite(dst_folder + '/' + ('C%d-' % _) + j, out * 255.)