forked from PulseFocusPlatform/PulseFocusPlatform
298 lines
10 KiB
Python
298 lines
10 KiB
Python
# coding: utf-8
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import division
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import cv2
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import numpy as np
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from PIL import Image, ImageDraw
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def visualize_box_mask(im, results, labels, mask_resolution=14, threshold=0.5):
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"""
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Args:
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im (str/np.ndarray): path of image/np.ndarray read by cv2
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results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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MaskRCNN's results include 'masks': np.ndarray:
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shape:[N, class_num, mask_resolution, mask_resolution]
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labels (list): labels:['class1', ..., 'classn']
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mask_resolution (int): shape of a mask is:[mask_resolution, mask_resolution]
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threshold (float): Threshold of score.
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Returns:
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im (PIL.Image.Image): visualized image
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"""
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if isinstance(im, str):
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im = Image.open(im).convert('RGB')
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else:
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im = Image.fromarray(im)
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if 'masks' in results and 'boxes' in results:
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im = draw_mask(
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im,
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results['boxes'],
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results['masks'],
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labels,
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resolution=mask_resolution)
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if 'boxes' in results:
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im = draw_box(im, results['boxes'], labels)
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if 'segm' in results:
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im = draw_segm(
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im,
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results['segm'],
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results['label'],
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results['score'],
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labels,
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threshold=threshold)
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if 'landmark' in results:
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im = draw_lmk(im, results['landmark'])
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return im
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def get_color_map_list(num_classes):
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"""
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Args:
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num_classes (int): number of class
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Returns:
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color_map (list): RGB color list
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"""
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color_map = num_classes * [0, 0, 0]
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for i in range(0, num_classes):
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j = 0
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lab = i
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while lab:
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color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
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color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
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color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
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j += 1
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lab >>= 3
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color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
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return color_map
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def expand_boxes(boxes, scale=0.0):
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"""
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Args:
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boxes (np.ndarray): shape:[N,4], N:number of box,
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matix element:[x_min, y_min, x_max, y_max]
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scale (float): scale of boxes
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Returns:
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boxes_exp (np.ndarray): expanded boxes
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"""
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w_half = (boxes[:, 2] - boxes[:, 0]) * .5
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h_half = (boxes[:, 3] - boxes[:, 1]) * .5
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x_c = (boxes[:, 2] + boxes[:, 0]) * .5
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y_c = (boxes[:, 3] + boxes[:, 1]) * .5
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w_half *= scale
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h_half *= scale
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boxes_exp = np.zeros(boxes.shape)
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boxes_exp[:, 0] = x_c - w_half
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boxes_exp[:, 2] = x_c + w_half
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boxes_exp[:, 1] = y_c - h_half
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boxes_exp[:, 3] = y_c + h_half
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return boxes_exp
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def draw_mask(im, np_boxes, np_masks, labels, resolution=14, threshold=0.5):
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"""
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Args:
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im (PIL.Image.Image): PIL image
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np_boxes (np.ndarray): shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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np_masks (np.ndarray): shape:[N, class_num, resolution, resolution]
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labels (list): labels:['class1', ..., 'classn']
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resolution (int): shape of a mask is:[resolution, resolution]
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threshold (float): threshold of mask
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Returns:
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im (PIL.Image.Image): visualized image
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"""
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color_list = get_color_map_list(len(labels))
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scale = (resolution + 2.0) / resolution
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im_w, im_h = im.size
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w_ratio = 0.4
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alpha = 0.7
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im = np.array(im).astype('float32')
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rects = np_boxes[:, 2:]
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expand_rects = expand_boxes(rects, scale)
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expand_rects = expand_rects.astype(np.int32)
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clsid_scores = np_boxes[:, 0:2]
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padded_mask = np.zeros((resolution + 2, resolution + 2), dtype=np.float32)
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clsid2color = {}
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for idx in range(len(np_boxes)):
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clsid, score = clsid_scores[idx].tolist()
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clsid = int(clsid)
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xmin, ymin, xmax, ymax = expand_rects[idx].tolist()
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w = xmax - xmin + 1
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h = ymax - ymin + 1
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w = np.maximum(w, 1)
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h = np.maximum(h, 1)
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padded_mask[1:-1, 1:-1] = np_masks[idx, int(clsid), :, :]
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resized_mask = cv2.resize(padded_mask, (w, h))
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resized_mask = np.array(resized_mask > threshold, dtype=np.uint8)
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x0 = min(max(xmin, 0), im_w)
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x1 = min(max(xmax + 1, 0), im_w)
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y0 = min(max(ymin, 0), im_h)
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y1 = min(max(ymax + 1, 0), im_h)
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im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
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im_mask[y0:y1, x0:x1] = resized_mask[(y0 - ymin):(y1 - ymin), (
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x0 - xmin):(x1 - xmin)]
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if clsid not in clsid2color:
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clsid2color[clsid] = color_list[clsid]
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color_mask = clsid2color[clsid]
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for c in range(3):
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color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
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idx = np.nonzero(im_mask)
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color_mask = np.array(color_mask)
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im[idx[0], idx[1], :] *= 1.0 - alpha
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im[idx[0], idx[1], :] += alpha * color_mask
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return Image.fromarray(im.astype('uint8'))
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def draw_box(im, np_boxes, labels):
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"""
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Args:
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im (PIL.Image.Image): PIL image
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np_boxes (np.ndarray): shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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labels (list): labels:['class1', ..., 'classn']
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Returns:
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im (PIL.Image.Image): visualized image
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"""
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draw_thickness = min(im.size) // 320
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draw = ImageDraw.Draw(im)
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clsid2color = {}
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color_list = get_color_map_list(len(labels))
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for dt in np_boxes:
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clsid, bbox, score = int(dt[0]), dt[2:], dt[1]
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xmin, ymin, xmax, ymax = bbox
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w = xmax - xmin
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h = ymax - ymin
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if clsid not in clsid2color:
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clsid2color[clsid] = color_list[clsid]
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color = tuple(clsid2color[clsid])
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# draw bbox
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draw.line(
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[(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
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(xmin, ymin)],
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width=draw_thickness,
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fill=color)
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# draw label
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text = "{} {:.4f}".format(labels[clsid], score)
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tw, th = draw.textsize(text)
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draw.rectangle(
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[(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
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draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
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return im
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def draw_segm(im,
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np_segms,
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np_label,
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np_score,
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labels,
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threshold=0.5,
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alpha=0.7):
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"""
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Draw segmentation on image
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"""
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mask_color_id = 0
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w_ratio = .4
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color_list = get_color_map_list(len(labels))
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im = np.array(im).astype('float32')
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clsid2color = {}
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np_segms = np_segms.astype(np.uint8)
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for i in range(np_segms.shape[0]):
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mask, score, clsid = np_segms[i], np_score[i], np_label[i] + 1
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if score < threshold:
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continue
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if clsid not in clsid2color:
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clsid2color[clsid] = color_list[clsid]
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color_mask = clsid2color[clsid]
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for c in range(3):
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color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
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idx = np.nonzero(mask)
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color_mask = np.array(color_mask)
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im[idx[0], idx[1], :] *= 1.0 - alpha
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im[idx[0], idx[1], :] += alpha * color_mask
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sum_x = np.sum(mask, axis=0)
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x = np.where(sum_x > 0.5)[0]
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sum_y = np.sum(mask, axis=1)
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y = np.where(sum_y > 0.5)[0]
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x0, x1, y0, y1 = x[0], x[-1], y[0], y[-1]
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cv2.rectangle(im, (x0, y0), (x1, y1),
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tuple(color_mask.astype('int32').tolist()), 1)
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bbox_text = '%s %.2f' % (labels[clsid], score)
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t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0]
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cv2.rectangle(im, (x0, y0), (x0 + t_size[0], y0 - t_size[1] - 3),
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tuple(color_mask.astype('int32').tolist()), -1)
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cv2.putText(
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im,
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bbox_text, (x0, y0 - 2),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.3, (0, 0, 0),
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1,
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lineType=cv2.LINE_AA)
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return Image.fromarray(im.astype('uint8'))
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def lmk2out(bboxes, np_lmk, im_info, threshold=0.5, is_bbox_normalized=True):
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image_w, image_h = im_info['origin_shape']
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scale = im_info['scale']
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face_index, landmark, prior_box = np_lmk[:]
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xywh_res = []
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if bboxes.shape == (1, 1) or bboxes is None:
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return np.array([])
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prior = np.reshape(prior_box, (-1, 4))
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predict_lmk = np.reshape(landmark, (-1, 10))
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k = 0
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for i in range(bboxes.shape[0]):
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score = bboxes[i][1]
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if score < threshold:
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continue
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theindex = face_index[i][0]
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me_prior = prior[theindex, :]
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lmk_pred = predict_lmk[theindex, :]
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prior_h = me_prior[2] - me_prior[0]
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prior_w = me_prior[3] - me_prior[1]
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prior_h_center = (me_prior[2] + me_prior[0]) / 2
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prior_w_center = (me_prior[3] + me_prior[1]) / 2
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lmk_decode = np.zeros((10))
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for j in [0, 2, 4, 6, 8]:
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lmk_decode[j] = lmk_pred[j] * 0.1 * prior_w + prior_h_center
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for j in [1, 3, 5, 7, 9]:
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lmk_decode[j] = lmk_pred[j] * 0.1 * prior_h + prior_w_center
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if is_bbox_normalized:
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lmk_decode = lmk_decode * np.array([
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image_h, image_w, image_h, image_w, image_h, image_w, image_h,
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image_w, image_h, image_w
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])
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xywh_res.append(lmk_decode)
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return np.asarray(xywh_res)
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def draw_lmk(image, lmk_results):
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draw = ImageDraw.Draw(image)
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for lmk_decode in lmk_results:
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for j in range(5):
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x1 = int(round(lmk_decode[2 * j]))
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y1 = int(round(lmk_decode[2 * j + 1]))
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draw.ellipse(
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(x1 - 2, y1 - 2, x1 + 3, y1 + 3), fill='green', outline='green')
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return image
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