Python cv2.COLORMAP_JET属性代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def show_landmarks(image, heatmap, gt_landmarks, gt_heatmap):
    """Show image with pred_landmarks"""
    pred_landmarks = []
    pred_landmarks, _ = get_preds_fromhm(torch.from_numpy(heatmap).unsqueeze(0))
    pred_landmarks = pred_landmarks.squeeze()*4

    # pred_landmarks2 = get_preds_fromhm2(heatmap)
    heatmap = np.max(gt_heatmap, axis=0)
    heatmap = heatmap / np.max(heatmap)
    # image = ski_transform.resize(image, (64, 64))*255
    image = image.astype(np.uint8)
    heatmap = np.max(gt_heatmap, axis=0)
    heatmap = ski_transform.resize(heatmap, (image.shape[0], image.shape[1]))
    heatmap *= 255
    heatmap = heatmap.astype(np.uint8)
    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
    plt.imshow(image)
    plt.scatter(gt_landmarks[:, 0], gt_landmarks[:, 1], s=0.5, marker='.', c='g')
    plt.scatter(pred_landmarks[:, 0], pred_landmarks[:, 1], s=0.5, marker='.', c='r')
    plt.pause(0.001)  # pause a bit so that plots are updated 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def test_heatmaps(heatmaps,img,i):
    heatmaps=heatmaps.numpy()
    #heatmaps=np.squeeze(heatmaps)
    heatmaps=heatmaps[:,:64,:]
    heatmaps=heatmaps.transpose(1,2,0)
    print('heatmap inside shape is',heatmaps.shape)
##    print('----------------here')
##    print(heatmaps.shape)
    img=img.numpy()
    #img=np.squeeze(img)
    img=img.transpose(1,2,0)
    img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#    print('heatmaps',heatmaps.shape)
    heatmaps = cv2.resize(heatmaps,(0,0), fx=4,fy=4)
#    print('heatmapsafter',heatmaps.shape)
    for j in range(0, 16):
        heatmap = heatmaps[:,:,j]
        heatmap = heatmap.reshape((256,256,1))
        heatmapimg = np.array(heatmap * 255, dtype = np.uint8)
        heatmap = cv2.applyColorMap(heatmapimg, cv2.COLORMAP_JET)
        heatmap = heatmap/255
        plt.imshow(img)
        plt.imshow(heatmap, alpha=0.5)
        plt.show()
        #plt.savefig('hmtestpadh36'+str(i)+js[j]+'.png') 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def calculate_gradient_weighted_CAM(gradient_function, image):
    output, evaluated_gradients = gradient_function([image, False])
    output, evaluated_gradients = output[0, :], evaluated_gradients[0, :, :, :]
    weights = np.mean(evaluated_gradients, axis=(0, 1))
    CAM = np.ones(output.shape[0: 2], dtype=np.float32)
    for weight_arg, weight in enumerate(weights):
        CAM = CAM + (weight * output[:, :, weight_arg])
    CAM = cv2.resize(CAM, (64, 64))
    CAM = np.maximum(CAM, 0)
    heatmap = CAM / np.max(CAM)

    # Return to BGR [0..255] from the preprocessed image
    image = image[0, :]
    image = image - np.min(image)
    image = np.minimum(image, 255)

    CAM = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)
    CAM = np.float32(CAM) + np.float32(image)
    CAM = 255 * CAM / np.max(CAM)
    return np.uint8(CAM), heatmap 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def calculate_gradient_weighted_CAM(gradient_function, image):
    output, evaluated_gradients = gradient_function([image, False])
    output, evaluated_gradients = output[0, :], evaluated_gradients[0, :, :, :]
    weights = np.mean(evaluated_gradients, axis = (0, 1))
    CAM = np.ones(output.shape[0 : 2], dtype=np.float32)
    for weight_arg, weight in enumerate(weights):
        CAM = CAM + (weight * output[:, :, weight_arg])
    CAM = cv2.resize(CAM, (64, 64))
    CAM = np.maximum(CAM, 0)
    heatmap = CAM / np.max(CAM)

    #Return to BGR [0..255] from the preprocessed image
    image = image[0, :]
    image = image - np.min(image)
    image = np.minimum(image, 255)

    CAM = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)
    CAM = np.float32(CAM) + np.float32(image)
    CAM = 255 * CAM / np.max(CAM)
    return np.uint8(CAM), heatmap 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def read_h5py_example():
    h5_in = h5py.File(os.path.join(dir_path, 'data.h5'), 'r')
    print (h5_in.keys())
    print (h5_in['train']['image'].dtype)
    print (h5_in['train']['image'][0].shape)

    image_size = h5_in['train']['image'].attrs['size']
    label_size = h5_in['train']['label'].attrs['size']

    x_img = np.reshape(h5_in['train']['image'][0], tuple(image_size))
    y_img = np.reshape(h5_in['train']['label'][0], tuple(label_size))
    name = h5_in['train']['name'][0]
    print (name)
    y_img = (y_img.astype(np.float32)*255/33).astype(np.uint8)
    y_show = cv2.applyColorMap(y_img, cv2.COLORMAP_JET)
    show = cv2.addWeighted(x_img, 0.5, y_show, 0.5, 0)
    cv2.imshow("show", show)
    cv2.waitKey() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def image_copy_to_dir(mode, x_paths, y_paths):
    target_path = '/run/media/tkwoo/myWorkspace/workspace/01.dataset/03.Mask_data/cityscape'
    target_path = os.path.join(target_path, mode)

    for idx in trange(len(x_paths)):
        image = cv2.imread(x_paths[idx], 1)
        mask = cv2.imread(y_paths[idx], 0)

        image = cv2.resize(image, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_LINEAR)
        mask = cv2.resize(mask, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)

        cv2.imwrite(os.path.join(target_path, 'image', os.path.basename(x_paths[idx])), image)
        cv2.imwrite(os.path.join(target_path, 'mask', os.path.basename(y_paths[idx])), mask)

        # show = image.copy()
        # mask = (mask.astype(np.float32)*255/33).astype(np.uint8)
        # mask_color = cv2.applyColorMap(mask, cv2.COLORMAP_JET)
        # show = cv2.addWeighted(show, 0.5, mask_color, 0.5, 0.0)
        # cv2.imshow('show', show)
        # key = cv2.waitKey(1)
        # if key == 27:
        #     return 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def train_generator(self, image_generator, mask_generator):
        # cv2.namedWindow('show', 0)
        # cv2.resizeWindow('show', 1280, 640)
        while True:
            image = next(image_generator)
            mask = next(mask_generator)
            label = self.make_regressor_label(mask).astype(np.float32)
            # print (image.dtype, label.dtype)
            # print (image.shape, label.shape)
            # exit()
            # cv2.imshow('show', image[0].astype(np.uint8))
            # cv2.imshow('label', label[0].astype(np.uint8))
            # mask = self.select_labels(mask)
            # print (image.shape)
            # print (mask.shape)
            # image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            # mask = (mask.astype(np.float32)*255/33).astype(np.uint8)
            # mask_color = cv2.applyColorMap(mask, cv2.COLORMAP_JET)
            # print (mask_color.shape)
            # show = cv2.addWeighted(image, 0.5, mask_color, 0.5, 0.0)
            # cv2.imshow("show", show)
            # key = cv2.waitKey()
            # if key == 27:
            #     exit()
            yield (image, label) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def draw_lines(imgs, lines, scores=None, width=2):
    assert len(imgs) == len(lines)
    imgs = np.uint8(imgs)
    bs = len(imgs)
    if scores is not None:
        assert len(scores) == bs
    res = []
    for b in range(bs):
        img = imgs[b].transpose((1, 2, 0))
        line = lines[b]
        if scores is None:
            score = np.zeros(len(line))
        else:
            score = scores[b]
        img = img.copy()
        for (x1, y1, x2, y2), c in zip(line, score):
            pt1, pt2 = (x1, y1), (x2, y2)
            c = tuple(cv2.applyColorMap(np.array(c * 255, dtype=np.uint8), cv2.COLORMAP_JET).flatten().tolist())
            img = cv2.line(img, pt1, pt2, c, width)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        res.append(th.from_numpy(img.transpose((2, 0, 1))))

    return res 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def render(self, n_max=0, fallback_im=None):
        if self.image_scores is not None:
            im = cv2.applyColorMap((self.image_scores * 255).astype(np.uint8),
                                   cv2.COLORMAP_JET)
        else:
            assert fallback_im is not None
            im = cv2.cvtColor(fallback_im, cv2.COLOR_GRAY2BGR)

        if n_max == 0:
            n_max = self.ips_rc.shape[1]
        for i in range(n_max):
            thickness_relevant_score = \
                np.clip(self.ip_scores[i], 0.2, 0.6) - 0.2
            thickness = int(thickness_relevant_score * 20)
            if type(self.scales) == np.ndarray:
                radius = int(self.scales[i] * 10)
            else:
                radius = 10
            cv2.circle(im, tuple(self.ips_rc[[1, 0], i]),
                       radius, (0, 255, 0), thickness, cv2.LINE_AA)
        return im 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def tile(net_outs, rows, cols, downscale, ips_rc=None):
    assert net_outs.shape[2] == 128
    xdim = net_outs.shape[1]
    ydim = net_outs.shape[0]

    im = np.zeros([rows * ydim, cols * xdim, 3])
    for r in range(rows):
        for c in range(cols):
            im_i = cv2.applyColorMap(
                (net_outs[:, :, r * cols + c] * 255).astype(np.uint8),
                cv2.COLORMAP_JET)
            if ips_rc is not None:
                cv2.circle(im_i, tuple(ips_rc[[1, 0], r * cols + c]),
                           downscale * 5, (0, 0, 0), downscale * 3,
                           cv2.LINE_AA)
            im[r * ydim:(r + 1) * ydim, c * xdim:(c + 1) * xdim, :] = im_i

    return skimage.measure.block_reduce(im, (downscale, downscale, 1), np.max) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def color_pro(pro, img=None, mode='hwc'):
	H, W = pro.shape
	pro_255 = (pro*255).astype(np.uint8)
	pro_255 = np.expand_dims(pro_255,axis=2)
	color = cv2.applyColorMap(pro_255,cv2.COLORMAP_JET)
	color = cv2.cvtColor(color, cv2.COLOR_BGR2RGB)
	if img is not None:
		rate = 0.5
		if mode == 'hwc':
			assert img.shape[0] == H and img.shape[1] == W
			color = cv2.addWeighted(img,rate,color,1-rate,0)
		elif mode == 'chw':
			assert img.shape[1] == H and img.shape[2] == W
			img = np.transpose(img,(1,2,0))
			color = cv2.addWeighted(img,rate,color,1-rate,0)
			color = np.transpose(color,(2,0,1))
	else:
		if mode == 'chw':
			color = np.transpose(color,(2,0,1))	
	return color 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def generate_colorbar(self, min_temp=None, max_temp=None, cmap=cv.COLORMAP_JET, height=None):
        if min_temp is None:
            min_temp = self.global_min_temp
        if max_temp is None:
            max_temp = self.global_max_temp
        cb_gray = np.arange(255,0,-1,dtype=np.uint8).reshape((255,1))
        if cmap is not None:
            cb_color = cv.applyColorMap(cb_gray, cmap)
        else:
            cb_color = cv.cvtColor(cb_gray, cv.COLOR_GRAY2BGR)
        for i in range(1,6):
            cb_color = np.concatenate( (cb_color, cb_color), axis=1 )
        
        if height is None:
            append_img = np.zeros( (self.thermal_image.shape[0], cb_color.shape[1]+30, 3), dtype=np.uint8 )
        else:
            append_img = np.zeros( (height, cb_color.shape[1]+30, 3), dtype=np.uint8 )

        append_img[append_img.shape[0]//2-cb_color.shape[0]//2  : append_img.shape[0]//2 - (cb_color.shape[0]//2) + cb_color.shape[0] , 10 : 10 + cb_color.shape[1] ] = cb_color
        cv.putText(append_img, str(min_temp), (5, append_img.shape[0]//2 - (cb_color.shape[0]//2) + cb_color.shape[0] + 30), cv.FONT_HERSHEY_PLAIN, 1, (255,0,0) , 1, 8)
        cv.putText(append_img, str(max_temp), (5, append_img.shape[0]//2-cb_color.shape[0]//2-20) , cv.FONT_HERSHEY_PLAIN, 1, (0,0,255) , 1, 8 )
        return append_img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def line_measurement(self, image, thermal_np, cmap=cv.COLORMAP_JET):
        img = image.copy()
        line, point1, point2 = CFlir.get_line(img)
        line_temps = np.zeros(len(line))
    
        if len(img.shape) == 3:
            gray_values = np.arange(256, dtype=np.uint8)
            color_values = map(tuple, cv.applyColorMap(gray_values, cmap).reshape(256, 3))
            color_to_gray_map = dict(zip(color_values, gray_values))
            img = np.apply_along_axis(lambda bgr: color_to_gray_map[tuple(bgr)], 2, image)
        
        for i in range(0,len(line)):
            line_temps[i] = thermal_np[ line[i][1], line[i][0] ]
            
        cv.line(img, point1, point2, 255, 2, 8)
        
        plt.subplot(1, 5, (1,2) )
        plt.imshow(img, cmap='jet')
        plt.title('Image')
        plt.subplot(1, 5, (4,5) )
        plt.plot(line_temps)
        plt.title('Distance vs Temperature')
        plt.show() 
        
        logger.info(f'\nMin line: {np.amin(line_temps)}\nMax line: {np.amax(line_temps)}' ) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def get_scaled_image(self, img, thermal_np, raw_thermal_np, cmap=cv.COLORMAP_JET, is_rect=False ) :
        self.scale_contours = []
        CFlir.contour=[]
        CFlir.get_contours(img, self.scale_contours)
        flag = False
        
        if len (self.scale_contours) > 0:
            
            if len(self.scale_contours[0]) > 15:
                flag = True
                thermal_roi_values = CFlir.get_roi(img, thermal_np, raw_thermal_np, self.scale_contours, 0)[1]
                temp_scaled = CFlir.scale_with_roi(thermal_np, thermal_roi_values)
                temp_scaled_image = CFlir.get_temp_image(temp_scaled, colormap=cmap)

        if flag == False:
            temp_scaled = thermal_np.copy()
            temp_scaled_image = CFlir.get_temp_image(temp_scaled, colormap=cmap)

        return temp_scaled , temp_scaled_image 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import COLORMAP_JET [as 别名]
def various_scale_attention_weights_visualize(spatial_weights,original_img1,original_img2,save_base_path,filename):

    nchannel, height,width = spatial_weights.shape
    scale_list = ['common','t0','t1']
    original_imgs = [original_img1,original_img1,original_img2]
    assert len(scale_list) == len(spatial_weights)
    for idx in range(nchannel):

        height_img, width_img, channel = original_imgs[idx].shape
        scale_x = spatial_weights[idx]
        scale_name = scale_list[idx]
        scalex_x_att_map = cv2.resize(scale_x,(width_img,height_img),interpolation=cv2.INTER_LINEAR)
        scalex_x_att_map_ = cv2.applyColorMap(np.uint8(255* scalex_x_att_map),cv2.COLORMAP_JET)
        fuse_scale_att_map = 0.6 * scalex_x_att_map_ + 0.4 * original_imgs[idx]
        cv2.imwrite(save_base_path + '_' + str(filename) + '_origin_' + str(scale_name) + '.jpg', scalex_x_att_map_)
        cv2.imwrite(save_base_path + '_' + str(filename) + '_fuse_' + str(scale_name) + '.jpg', fuse_scale_att_map)