Python utils.resize_image方法代码示例

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def get_rcnn_detection(self,image_t):
        image_t_resized, window, scale, padding, crop = utils.resize_image(
                        np.copy(image_t),
                        min_dim=self.config.IMAGE_MIN_DIM,
                        min_scale=self.config.IMAGE_MIN_SCALE,
                        max_dim=self.config.IMAGE_MAX_DIM,
                        mode=self.config.IMAGE_RESIZE_MODE)
        if(scale!=1):
            print("Warning.. have to adjust the scale")        
        results = self.detection_model.detect([image_t_resized], verbose=0)
        r = results[0]
        rois = r['rois']
        rois = rois - [window[0],window[1],window[0],window[1]]
        obj_orders = np.array(r['class_ids'])-1
        obj_ids=[]
        for obj_order in obj_orders:
            obj_ids.append(self.detection_labels[obj_order])
        #now c_ids are the same annotation those of the names of ply/gt files
        scores = np.array(r['scores'])
        masks = r['masks'][window[0]:window[2],window[1]:window[3],:]
        return rois,obj_orders,obj_ids,scores,masks 

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def get_rcnn_detection(image_t,model):
        image_t_resized, window, scale, padding, crop = utils.resize_image(
                        np.copy(image_t),
                        min_dim=config.IMAGE_MIN_DIM,
                        min_scale=config.IMAGE_MIN_SCALE,
                        max_dim=config.IMAGE_MAX_DIM,
                        mode=config.IMAGE_RESIZE_MODE)
        if(scale!=1):
            print("Warning.. have to adjust the scale")        
        results = model.detect([image_t_resized], verbose=0)
        r = results[0]
        rois = r['rois']
        rois = rois - [window[0],window[1],window[0],window[1]]
        obj_orders = np.array(r['class_ids'])-1
        obj_ids = model_ids[obj_orders] 
        #now c_ids are the same annotation those of the names of ply/gt files
        scores = np.array(r['scores'])
        masks = r['masks'][window[0]:window[2],window[1]:window[3],:]
        return rois,obj_orders,obj_ids,scores,masks 

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def mold_inputs(self, images):
        """Takes a list of images and modifies them to the format expected
        as an input to the neural network.
        images: List of image matrices [height,width,depth]. Images can have
            different sizes.
        Returns 3 Numpy matrices:
        molded_images: [N, h, w, 3]. Images resized and normalized.
        image_metas: [N, length of meta data]. Details about each image.
        windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the
            original image (padding excluded).
        """
        molded_images = []
        image_metas = []
        windows = []
        for image in images:
            # Resize image
            # TODO: move resizing to mold_image()
            molded_image, window, scale, padding, crop = utils.resize_image(
                image,
                min_dim=self.config.IMAGE_MIN_DIM,
                min_scale=self.config.IMAGE_MIN_SCALE,
                max_dim=self.config.IMAGE_MAX_DIM,
                mode=self.config.IMAGE_RESIZE_MODE)
            molded_image = mold_image(molded_image, self.config)
            # Build image_meta
            image_meta = compose_image_meta(
                0, image.shape, molded_image.shape, window, scale,
                np.zeros([self.config.NUM_CLASSES], dtype=np.int32))
            # Append
            molded_images.append(molded_image)
            windows.append(window)
            image_metas.append(image_meta)
        # Pack into arrays
        molded_images = np.stack(molded_images)
        image_metas = np.stack(image_metas)
        windows = np.stack(windows)
        return molded_images, image_metas, windows 

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def get_rcnn_detection(image_t,model):
        image_t_resized, window, scale, padding, crop = utils.resize_image(
                        np.copy(image_t),
                        min_dim=config.IMAGE_MIN_DIM,
                        min_scale=config.IMAGE_MIN_SCALE,
                        max_dim=config.IMAGE_MAX_DIM,
                        mode=config.IMAGE_RESIZE_MODE)
        if(scale!=1):
            print("Warning.. have to adjust the scale")        
        results = model.detect([image_t_resized], verbose=0)
        r = results[0]
        rois = r['rois']
        if(scale!=1):
            masks_all = r['masks'][window[0]:window[2],window[1]:window[3],:]
            masks = np.zeros((image_t.shape[0],image_t.shape[1],masks_all.shape[2]),bool)
            for mask_id in range(masks_all.shape[2]):
                masks[:,:,mask_id]=resize(masks_all[:,:,mask_id].astype(np.float),(image_t.shape[0],image_t.shape[1]))>0.5
            #resize all the masks            
            rois=rois/scale
            window = np.array(window)
            window[0] = window[0]/scale
            window[1] = window[1]/scale
            window[2] = window[2]/scale
            window[3] = window[3]/scale     
        else:
            masks = r['masks'][window[0]:window[2],window[1]:window[3],:]

        rois = rois - [window[0],window[1],window[0],window[1]]
        obj_orders = np.array(r['class_ids'])-1
        obj_ids = model_ids[obj_orders] 
        #now c_ids are the same annotation those of the names of ply/gt files
        scores = np.array(r['scores'])        
        return rois,obj_orders,obj_ids,scores,masks 

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def get_retinanet_detection(image_t,model):
        image = preprocess_image(image_t[:,:,::-1]) #needs bgr order bgr?
        image, scale = resize_image(image)
        boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
        boxes /= scale
        boxes = boxes[0]
        scores = scores[0]
        labels = labels[0]
        
        score_mask = scores>0
        if(np.sum(score_mask)==0):
            return np.array([[-1,-1,-1,-1]]),-1,-1,-1

        else:            
            scores = scores[score_mask]
            boxes =  boxes[score_mask]
            labels =  labels[score_mask]
            
            rois = np.zeros((boxes.shape[0],4),np.int)
            rois[:,0] = boxes[:,1]
            rois[:,1] = boxes[:,0]
            rois[:,2] = boxes[:,3]
            rois[:,3] = boxes[:,2]
            obj_orders = labels 
            obj_ids = model_ids[obj_orders]            

            return rois,obj_orders,obj_ids,scores 

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def mold_inputs(self, images):
        """Takes a list of images and modifies them to the format expected
        as an input to the neural network.
        images: List of image matrices [height,width,depth]. Images can have
            different sizes.

        Returns 3 Numpy matrices:
        molded_images: [N, h, w, 3]. Images resized and normalized.
        image_metas: [N, length of meta data]. Details about each image.
        windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the
            original image (padding excluded).
        """
        molded_images = []
        image_metas = []
        windows = []
        for image in images:
            # Resize image
            # TODO: move resizing to mold_image()
            molded_image, window, scale, padding, crop = utils.resize_image(
                image,
                min_dim=self.config.IMAGE_MIN_DIM,
                min_scale=self.config.IMAGE_MIN_SCALE,
                max_dim=self.config.IMAGE_MAX_DIM,
                mode=self.config.IMAGE_RESIZE_MODE)
            molded_image = mold_image(molded_image, self.config)
            # Build image_meta
            image_meta = compose_image_meta(
                0, image.shape, molded_image.shape, window, scale,
                np.zeros([self.config.NUM_CLASSES], dtype=np.int32))
            # Append
            molded_images.append(molded_image)
            windows.append(window)
            image_metas.append(image_meta)
        # Pack into arrays
        molded_images = np.stack(molded_images)
        image_metas = np.stack(image_metas)
        windows = np.stack(windows)
        return molded_images, image_metas, windows 

# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import resize_image [as 别名]
def mold_inputs(self, images):
        """Takes a list of images and modifies them to the format expected
        as an input to the neural network.
        images: List of image matricies [height,width,depth]. Images can have
            different sizes.

        Returns 3 Numpy matricies:
        molded_images: [N, h, w, 3]. Images resized and normalized.
        image_metas: [N, length of meta data]. Details about each image.
        windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the
            original image (padding excluded).
        """
        molded_images = []
        image_metas = []
        windows = []
        for image in images:
            # Resize image
            # TODO: move resizing to mold_image()
            molded_image, window, scale, padding, crop = utils.resize_image(
                image,
                min_dim=self.config.IMAGE_MIN_DIM,
                min_scale=self.config.IMAGE_MIN_SCALE,
                max_dim=self.config.IMAGE_MAX_DIM,
                mode=self.config.IMAGE_RESIZE_MODE)
            molded_image = mold_image(molded_image, self.config)
            # Build image_meta
            image_meta = compose_image_meta(
                0, image.shape, molded_image.shape, window, scale,
                np.zeros([self.config.NUM_CLASSES], dtype=np.int32))
            # Append
            molded_images.append(molded_image)
            windows.append(window)
            image_metas.append(image_meta)
        # Pack into arrays
        molded_images = np.stack(molded_images)
        image_metas = np.stack(image_metas)
        windows = np.stack(windows)
        return molded_images, image_metas, windows