updata optflow

contrib/HumanSeg/utils/humanseg_postprocess.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# 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 numpy as np
 import cv2
+import os
+
 
+def get_round(data):
+    round = 0.5 if data >= 0 else -0.5
+    return (int)(data + round)
 
-def humanseg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
+
+def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
     """计算光流跟踪匹配点和光流图
     输入参数:
         pre_gray: 上一帧灰度图
@@ -31,131 +21,102 @@ def humanseg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
         track_cfd: 光流跟踪图
     """
     check_thres = 8
-    hgt, wdh = pre_gray.shape[:2]
+    h, w = pre_gray.shape[:2]
     track_cfd = np.zeros_like(prev_cfd)
     is_track = np.zeros_like(pre_gray)
-    # 计算前向光流
     flow_fw = disflow.calc(pre_gray, cur_gray, None)
-    # 计算后向光流
     flow_bw = disflow.calc(cur_gray, pre_gray, None)
-    get_round = lambda data: (int)(data + 0.5) if data >= 0 else (int)(data -
-                                                                       0.5)
-    for row in range(hgt):
-        for col in range(wdh):
-            # 计算光流处理后对应点坐标
-            # (row, col) -> (cur_x, cur_y)
-            fxy_fw = flow_fw[row, col]
+    for r in range(h):
+        for c in range(w):
+            fxy_fw = flow_fw[r, c]
             dx_fw = get_round(fxy_fw[0])
-            cur_x = dx_fw + col
+            cur_x = dx_fw + c
             dy_fw = get_round(fxy_fw[1])
-            cur_y = dy_fw + row
-            if cur_x < 0 or cur_x >= wdh or cur_y < 0 or cur_y >= hgt:
+            cur_y = dy_fw + r
+            if cur_x < 0 or cur_x >= w or cur_y < 0 or cur_y >= h:
                 continue
             fxy_bw = flow_bw[cur_y, cur_x]
             dx_bw = get_round(fxy_bw[0])
             dy_bw = get_round(fxy_bw[1])
-            # 光流移动小于阈值
-            lmt = ((dy_fw + dy_bw) * (dy_fw + dy_bw) +
-                   (dx_fw + dx_bw) * (dx_fw + dx_bw))
-            if lmt >= check_thres:
+            if ((dy_fw + dy_bw) * (dy_fw + dy_bw) +
+                (dx_fw + dx_bw) * (dx_fw + dx_bw)) >= check_thres:
                 continue
-            # 静止点降权
             if abs(dy_fw) <= 0 and abs(dx_fw) <= 0 and abs(dy_bw) <= 0 and abs(
                     dx_bw) <= 0:
                 dl_weights[cur_y, cur_x] = 0.05
             is_track[cur_y, cur_x] = 1
-            track_cfd[cur_y, cur_x] = prev_cfd[row, col]
+            track_cfd[cur_y, cur_x] = prev_cfd[r, c]
     return track_cfd, is_track, dl_weights
 
 
-def humanseg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
+def human_seg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
     """光流追踪图和人像分割结构融合
     输入参数:
         track_cfd: 光流追踪图
         dl_cfd: 当前帧分割结果
         dl_weights: 融合权重图
         is_track: 光流点匹配二值图
-    返回值:
+    返回
         cur_cfd: 光流跟踪图和人像分割结果融合图
     """
-    cur_cfd = dl_cfd.copy()
+    fusion_cfd = dl_cfd.copy()
     idxs = np.where(is_track > 0)
-    for i in range(len(idxs)):
+    for i in range(len(idxs[0])):
         x, y = idxs[0][i], idxs[1][i]
         dl_score = dl_cfd[x, y]
         track_score = track_cfd[x, y]
+        fusion_cfd[x, y] = dl_weights[x, y] * dl_score + (
+            1 - dl_weights[x, y]) * track_score
         if dl_score > 0.9 or dl_score < 0.1:
             if dl_weights[x, y] < 0.1:
-                cur_cfd[x, y] = 0.3 * dl_score + 0.7 * track_score
+                fusion_cfd[x, y] = 0.3 * dl_score + 0.7 * track_score
             else:
-                cur_cfd[x, y] = 0.4 * dl_score + 0.6 * track_score
+                fusion_cfd[x, y] = 0.4 * dl_score + 0.6 * track_score
         else:
-            cur_cfd[x, y] = dl_weights[x, y] * dl_score + (
+            fusion_cfd[x, y] = dl_weights[x, y] * dl_score + (
                 1 - dl_weights[x, y]) * track_score
-    return cur_cfd
-
-
-def threshold_mask(img, thresh_bg, thresh_fg):
-    """设置背景和前景阈值mask
-    输入参数:
-        img : 原始图像, np.uint8 类型.
-        thresh_bg : 背景阈值百分比，低于该值置为0.
-        thresh_fg : 前景阈值百分比，超过该值置为1.
-    返回值:
-        dst : 原始图像设置完前景背景阈值mask结果, np.float32 类型.
-    """
-    dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
-    dst[np.where(dst > 1)] = 1
-    dst[np.where(dst < 0)] = 0
-    return dst.astype(np.float32)
+    return fusion_cfd
 
 
-def optflow_handle(cur_gray, scoremap, is_init):
+def postprocess(cur_gray, scoremap, prev_gray, pre_cfd, disflow, is_init):
     """光流优化
     Args:
         cur_gray : 当前帧灰度图
+        pre_gray : 前一帧灰度图
+        pre_cfd  ：前一帧融合结果
         scoremap : 当前帧分割结果
+        difflow  : 光流
         is_init : 是否第一帧
     Returns:
-        dst : 光流追踪图和预测结果融合图, 类型为 np.float32
+        fusion_cfd : 光流追踪图和预测结果融合图
     """
     height, width = scoremap.shape[0], scoremap.shape[1]
     disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
-    prev_gray = np.zeros((height, width), np.uint8)
-    prev_cfd = np.zeros((height, width), np.float32)
+    h, w = scoremap.shape
     cur_cfd = scoremap.copy()
+
     if is_init:
         is_init = False
-        if height <= 64 or width <= 64:
+        if h <= 64 or w <= 64:
             disflow.setFinestScale(1)
-        elif height <= 160 or width <= 160:
+        elif h <= 160 or w <= 160:
             disflow.setFinestScale(2)
         else:
             disflow.setFinestScale(3)
         fusion_cfd = cur_cfd
     else:
-        weights = np.ones((height, width), np.float32) * 0.3
-        track_cfd, is_track, weights = humanseg_tracking(
-            prev_gray, cur_gray, prev_cfd, weights, disflow)
-        fusion_cfd = humanseg_track_fuse(track_cfd, cur_cfd, weights, is_track)
+        weights = np.ones((w, h), np.float32) * 0.3
+        track_cfd, is_track, weights = human_seg_tracking(
+            prev_gray, cur_gray, pre_cfd, weights, disflow)
+        fusion_cfd = human_seg_track_fuse(track_cfd, cur_cfd, weights, is_track)
+
     fusion_cfd = cv2.GaussianBlur(fusion_cfd, (3, 3), 0)
+
     return fusion_cfd
 
 
-def postprocess(image, output_data):
-    """对预测结果进行后处理
-    Args:
-         image: 原始图，opencv 图片对象
-         output_data: Paddle预测结果原始数据
-    Returns:
-         原图和预测结果融合并做了光流优化的结果图
-    """
-    scoremap = output_data[:, :, 1]
-    scoremap = (scoremap * 255).astype(np.uint8)
-    # 光流处理
-    cur_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    optflow_map = optflow_handle(cur_gray, scoremap, False)
-    optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
-    optflow_map = threshold_mask(optflow_map, thresh_bg=0.2, thresh_fg=0.8)
-    optflow_map = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2)
-    return optflow_map
+def threshold_mask(img, thresh_bg, thresh_fg):
+    dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
+    dst[np.where(dst > 1)] = 1
+    dst[np.where(dst < 0)] = 0
+    return dst.astype(np.float32)

contrib/HumanSeg/video_infer.py

@@ -4,7 +4,7 @@ import os.path as osp
 import cv2
 import numpy as np
 
-from utils.humanseg_postprocess import postprocess
+from utils.humanseg_postprocess import postprocess, threshold_mask
 import models
 import transforms
 
@@ -60,8 +60,12 @@ def recover(img, im_info):
 
 
 def video_infer(args):
+
+    resize_h = 192
+    resize_w = 192
+
     test_transforms = transforms.Compose(
-        [transforms.Resize((192, 192)),
+        [transforms.Resize((resize_w, resize_h)),
          transforms.Normalize()])
     model = models.load_model(args.model_dir)
     if not args.video_path:
@@ -73,10 +77,18 @@ def video_infer(args):
                       "--video_path whether existing: {}"
                       " or camera whether working".format(args.video_path))
         return
-    if args.video_path:
+
     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
+    prev_gray = np.zeros((resize_h, resize_w), np.uint8)
+    prev_cfd = np.zeros((resize_h, resize_w), np.float32)
+    is_init = True
+
     fps = cap.get(cv2.CAP_PROP_FPS)
+    if args.video_path:
+
         # 用于保存预测结果视频
         if not osp.exists(args.save_dir):
             os.makedirs(args.save_dir)
@@ -88,8 +100,18 @@ def video_infer(args):
             ret, frame = cap.read()
             if ret:
                 score_map, im_info = predict(frame, model, test_transforms)
-                img = cv2.resize(frame, (192, 192))
-                img_mat = postprocess(img, score_map)
+                cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
+                scoremap = 255 * score_map[:, :, 1]
+                optflow_map = postprocess(cur_gray, scoremap, prev_gray, prev_cfd, \
+                        disflow, is_init)
+                prev_gray = cur_gray.copy()
+                prev_cfd = optflow_map.copy()
+                is_init = False
+                optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
+                optflow_map = threshold_mask(
+                    optflow_map, thresh_bg=0.2, thresh_fg=0.8)
+                img_mat = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2)
                 img_mat = recover(img_mat, im_info)
                 bg_im = np.ones_like(img_mat) * 255
                 comb = (img_mat * frame + (1 - img_mat) * bg_im).astype(
@@ -105,8 +127,19 @@ def video_infer(args):
             ret, frame = cap.read()
             if ret:
                 score_map, im_info = predict(frame, model, test_transforms)
-                img = cv2.resize(frame, (192, 192))
-                img_mat = postprocess(img, score_map)
+                cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
+                scoremap = 255 * score_map[:, :, 1]
+                optflow_map = postprocess(cur_gray, scoremap, prev_gray, prev_cfd, \
+                                          disflow, is_init)
+                prev_gray = cur_gray.copy()
+                prev_cfd = optflow_map.copy()
+                is_init = False
+                # optflow_map = optflow_map/255.0
+                optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
+                optflow_map = threshold_mask(
+                    optflow_map, thresh_bg=0.2, thresh_fg=0.8)
+                img_mat = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2)
                 img_mat = recover(img_mat, im_info)
                 bg_im = np.ones_like(img_mat) * 255
                 comb = (img_mat * frame + (1 - img_mat) * bg_im).astype(