surveillance_monitoring/dependence/PaddleDetection/static/application/christmas/blazeface/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']
    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)
    if 'landmark' in results:
        im = draw_lmk(im, results['landmark'])
    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


def lmk2out(bboxes, np_lmk, im_info, threshold=0.5, is_bbox_normalized=True):
    image_w, image_h = im_info['origin_shape']
    scale = im_info['scale']
    face_index, landmark, prior_box = np_lmk[:]
    xywh_res = []
    if bboxes.shape == (1, 1) or bboxes is None:
        return np.array([])
    prior = np.reshape(prior_box, (-1, 4))
    predict_lmk = np.reshape(landmark, (-1, 10))
    k = 0
    for i in range(bboxes.shape[0]):
        score = bboxes[i][1]
        if score < threshold:
            continue
        theindex = face_index[i][0]
        me_prior = prior[theindex, :]
        lmk_pred = predict_lmk[theindex, :]
        prior_h = me_prior[2] - me_prior[0]
        prior_w = me_prior[3] - me_prior[1]
        prior_h_center = (me_prior[2] + me_prior[0]) / 2
        prior_w_center = (me_prior[3] + me_prior[1]) / 2
        lmk_decode = np.zeros((10))
        for j in [0, 2, 4, 6, 8]:
            lmk_decode[j] = lmk_pred[j] * 0.1 * prior_w + prior_h_center
        for j in [1, 3, 5, 7, 9]:
            lmk_decode[j] = lmk_pred[j] * 0.1 * prior_h + prior_w_center

        if is_bbox_normalized:
            lmk_decode = lmk_decode * np.array([
                image_h, image_w, image_h, image_w, image_h, image_w, image_h,
                image_w, image_h, image_w
            ])
        xywh_res.append(lmk_decode)
    return np.asarray(xywh_res)


def draw_lmk(image, lmk_results):
    draw = ImageDraw.Draw(image)
    for lmk_decode in lmk_results:
        for j in range(5):
            x1 = int(round(lmk_decode[2 * j]))
            y1 = int(round(lmk_decode[2 * j + 1]))
            draw.ellipse(
                (x1 - 2, y1 - 2, x1 + 3, y1 + 3), fill='green', outline='green')
    return image