surveillance_monitoring/dependence/PaddleDetection/static/application/christmas/solov2/data_feed.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import base64

import cv2
import numpy as np
from PIL import Image, ImageDraw
import paddle.fluid as fluid


def create_inputs(im, im_info):
    """generate input for different model type
    Args:
        im (np.ndarray): image (np.ndarray)
        im_info (dict): info of image
    Returns:
        inputs (dict): input of model
    """
    inputs = {}
    inputs['image'] = im
    origin_shape = list(im_info['origin_shape'])
    resize_shape = list(im_info['resize_shape'])
    pad_shape = list(im_info['pad_shape']) if im_info[
        'pad_shape'] is not None else list(im_info['resize_shape'])
    scale_x, scale_y = im_info['scale']
    scale = scale_x
    im_info = np.array([resize_shape + [scale]]).astype('float32')
    inputs['im_info'] = im_info
    return inputs


def visualize_box_mask(im,
                       results,
                       labels=None,
                       mask_resolution=14,
                       threshold=0.5):
    """
    Args:
        im (str/np.ndarray): path of image/np.ndarray read by cv2
        results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
                        matix element:[class, score, x_min, y_min, x_max, y_max]
                        MaskRCNN's results include 'masks': np.ndarray:
                        shape:[N, class_num, mask_resolution, mask_resolution]
        labels (list): labels:['class1', ..., 'classn']
        mask_resolution (int): shape of a mask is:[mask_resolution, mask_resolution]
        threshold (float): Threshold of score.
    Returns:
        im (PIL.Image.Image): visualized image
    """
    if not labels:
        labels = [
            'background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
            'bus', 'train', 'truck', 'boat', 'traffic light', 'fire', 'hydrant',
            'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
            'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
            'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
            'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
            'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
            'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
            'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
            'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
            'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
            'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
            'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
            'scissors', 'teddy bear', 'hair drier', 'toothbrush'
        ]
    if isinstance(im, str):
        im = Image.open(im).convert('RGB')
    else:
        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
        im = Image.fromarray(im)
    if 'masks' in results and 'boxes' in results:
        im = draw_mask(
            im,
            results['boxes'],
            results['masks'],
            labels,
            resolution=mask_resolution)
    if 'boxes' in results:
        im = draw_box(im, results['boxes'], labels)
    if 'segm' in results:
        im = draw_segm(
            im,
            results['segm'],
            results['label'],
            results['score'],
            labels,
            threshold=threshold)
    return im


def get_color_map_list(num_classes):
    """
    Args:
        num_classes (int): number of class
    Returns:
        color_map (list): RGB color list
    """
    color_map = num_classes * [0, 0, 0]
    for i in range(0, num_classes):
        j = 0
        lab = i
        while lab:
            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
            j += 1
            lab >>= 3
    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
    return color_map


def expand_boxes(boxes, scale=0.0):
    """
    Args:
        boxes (np.ndarray): shape:[N,4], N:number of box,
                            matix element:[x_min, y_min, x_max, y_max]
        scale (float): scale of boxes
    Returns:
        boxes_exp (np.ndarray): expanded boxes
    """
    w_half = (boxes[:, 2] - boxes[:, 0]) * .5
    h_half = (boxes[:, 3] - boxes[:, 1]) * .5
    x_c = (boxes[:, 2] + boxes[:, 0]) * .5
    y_c = (boxes[:, 3] + boxes[:, 1]) * .5
    w_half *= scale
    h_half *= scale
    boxes_exp = np.zeros(boxes.shape)
    boxes_exp[:, 0] = x_c - w_half
    boxes_exp[:, 2] = x_c + w_half
    boxes_exp[:, 1] = y_c - h_half
    boxes_exp[:, 3] = y_c + h_half
    return boxes_exp


def draw_mask(im, np_boxes, np_masks, labels, resolution=14, threshold=0.5):
    """
    Args:
        im (PIL.Image.Image): PIL image
        np_boxes (np.ndarray): shape:[N,6], N: number of box,
                               matix element:[class, score, x_min, y_min, x_max, y_max]
        np_masks (np.ndarray): shape:[N, class_num, resolution, resolution]
        labels (list): labels:['class1', ..., 'classn']
        resolution (int): shape of a mask is:[resolution, resolution]
        threshold (float): threshold of mask
    Returns:
        im (PIL.Image.Image): visualized image
    """
    color_list = get_color_map_list(len(labels))
    scale = (resolution + 2.0) / resolution
    im_w, im_h = im.size
    w_ratio = 0.4
    alpha = 0.7
    im = np.array(im).astype('float32')
    rects = np_boxes[:, 2:]
    expand_rects = expand_boxes(rects, scale)
    expand_rects = expand_rects.astype(np.int32)
    clsid_scores = np_boxes[:, 0:2]
    padded_mask = np.zeros((resolution + 2, resolution + 2), dtype=np.float32)
    clsid2color = {}
    for idx in range(len(np_boxes)):
        clsid, score = clsid_scores[idx].tolist()
        clsid = int(clsid)
        xmin, ymin, xmax, ymax = expand_rects[idx].tolist()
        w = xmax - xmin + 1
        h = ymax - ymin + 1
        w = np.maximum(w, 1)
        h = np.maximum(h, 1)
        padded_mask[1:-1, 1:-1] = np_masks[idx, int(clsid), :, :]
        resized_mask = cv2.resize(padded_mask, (w, h))
        resized_mask = np.array(resized_mask > threshold, dtype=np.uint8)
        x0 = min(max(xmin, 0), im_w)
        x1 = min(max(xmax + 1, 0), im_w)
        y0 = min(max(ymin, 0), im_h)
        y1 = min(max(ymax + 1, 0), im_h)
        im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
        im_mask[y0:y1, x0:x1] = resized_mask[(y0 - ymin):(y1 - ymin), (
            x0 - xmin):(x1 - xmin)]
        if clsid not in clsid2color:
            clsid2color[clsid] = color_list[clsid]
        color_mask = clsid2color[clsid]
        for c in range(3):
            color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
        idx = np.nonzero(im_mask)
        color_mask = np.array(color_mask)
        im[idx[0], idx[1], :] *= 1.0 - alpha
        im[idx[0], idx[1], :] += alpha * color_mask
    return Image.fromarray(im.astype('uint8'))


def draw_box(im, np_boxes, labels):
    """
    Args:
        im (PIL.Image.Image): PIL image
        np_boxes (np.ndarray): shape:[N,6], N: number of box,
                               matix element:[class, score, x_min, y_min, x_max, y_max]
        labels (list): labels:['class1', ..., 'classn']
    Returns:
        im (PIL.Image.Image): visualized image
    """
    draw_thickness = min(im.size) // 320
    draw = ImageDraw.Draw(im)
    clsid2color = {}
    color_list = get_color_map_list(len(labels))

    for dt in np_boxes:
        clsid, bbox, score = int(dt[0]), dt[2:], dt[1]
        xmin, ymin, xmax, ymax = bbox
        w = xmax - xmin
        h = ymax - ymin
        if clsid not in clsid2color:
            clsid2color[clsid] = color_list[clsid]
        color = tuple(clsid2color[clsid])

        # draw bbox
        draw.line(
            [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
             (xmin, ymin)],
            width=draw_thickness,
            fill=color)

        # draw label
        text = "{} {:.4f}".format(labels[clsid], score)
        tw, th = draw.textsize(text)
        draw.rectangle(
            [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
        draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
    return im


def draw_segm(im,
              np_segms,
              np_label,
              np_score,
              labels,
              threshold=0.5,
              alpha=0.7):
    """
    Draw segmentation on image
    """
    mask_color_id = 0
    w_ratio = .4
    color_list = get_color_map_list(len(labels))
    im = np.array(im).astype('float32')
    clsid2color = {}
    np_segms = np_segms.astype(np.uint8)
    index = np.where(np_label == 0)[0]
    index = np.where(np_score[index] > threshold)[0]
    person_segms = np_segms[index]
    person_mask = np.sum(person_segms, axis=0)
    person_mask[person_mask > 1] = 1
    person_mask = np.expand_dims(person_mask, axis=2)
    person_mask = np.repeat(person_mask, 3, axis=2)
    im = im * person_mask

    return Image.fromarray(im.astype('uint8'))


def load_predictor(model_dir,
                   run_mode='fluid',
                   batch_size=1,
                   use_gpu=False,
                   min_subgraph_size=3):
    """set AnalysisConfig, generate AnalysisPredictor
    Args:
        model_dir (str): root path of __model__ and __params__
        use_gpu (bool): whether use gpu
    Returns:
        predictor (PaddlePredictor): AnalysisPredictor
    Raises:
        ValueError: predict by TensorRT need use_gpu == True.
    """
    if not use_gpu and not run_mode == 'fluid':
        raise ValueError(
            "Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}"
            .format(run_mode, use_gpu))
    if run_mode == 'trt_int8':
        raise ValueError("TensorRT int8 mode is not supported now, "
                         "please use trt_fp32 or trt_fp16 instead.")
    precision_map = {
        'trt_int8': fluid.core.AnalysisConfig.Precision.Int8,
        'trt_fp32': fluid.core.AnalysisConfig.Precision.Float32,
        'trt_fp16': fluid.core.AnalysisConfig.Precision.Half
    }
    config = fluid.core.AnalysisConfig(
        os.path.join(model_dir, '__model__'),
        os.path.join(model_dir, '__params__'))
    if use_gpu:
        # initial GPU memory(M), device ID
        config.enable_use_gpu(100, 0)
        # optimize graph and fuse op
        config.switch_ir_optim(True)
    else:
        config.disable_gpu()

    if run_mode in precision_map.keys():
        config.enable_tensorrt_engine(
            workspace_size=1 << 10,
            max_batch_size=batch_size,
            min_subgraph_size=min_subgraph_size,
            precision_mode=precision_map[run_mode],
            use_static=False,
            use_calib_mode=False)

    # disable print log when predict
    config.disable_glog_info()
    # enable shared memory
    config.enable_memory_optim()
    # disable feed, fetch OP, needed by zero_copy_run
    config.switch_use_feed_fetch_ops(False)
    predictor = fluid.core.create_paddle_predictor(config)
    return predictor


def cv2_to_base64(image):
    data = cv2.imencode('.jpg', image)[1]
    return base64.b64encode(data.tostring()).decode('utf8')


def base64_to_cv2(b64str):
    data = base64.b64decode(b64str.encode('utf8'))
    data = np.fromstring(data, np.uint8)
    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
    return data