finish data checking

JesseYang · Apr 5, 2018 · 129e274 · 129e274
1 parent e945ffc
commit 129e274
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Showing 7 changed files with 1,132 additions and 860 deletions.
diff --git a/cfgs/config_default.py b/cfgs/config_default.py
@@ -20,6 +20,9 @@
 _.img_y = 368
 _.img_x = 368
 
+# whether skip a person if the distance to existing person is too small
+_.skip_adj = True
+
 
 # the default oder in coco annotation is:
 # 0:nose          1:left_eye         2:right_eye         3:left_ear

diff --git a/check_augmented_data.ipynb b/check_augmented_data.ipynb
diff --git a/check_data.ipynb b/check_data.ipynb
diff --git a/check_raw_data.ipynb b/check_raw_data.ipynb
diff --git a/coco_usage_demo.ipynb b/coco_usage_demo.ipynb
diff --git a/generate_dataset.py b/generate_dataset.py
@@ -1,8 +1,7 @@
 import os
 import cv2
 import sys
-sys.path.insert(1, '../data/coco/PythonAPI/')
-sys.path.insert(1, '../multi/')
+sys.path.insert(1, '../coco/cocoapi/PythonAPI/')
 from scipy.spatial.distance import cdist
 import numpy as np
 import pickle
@@ -36,6 +35,9 @@ def load_dataset(ann_path, images_dir, masks_dir, labels_dir):
     coco = COCO(ann_path)
     for img_id in tqdm(coco.imgs.keys(), ascii=True):
 
+        if img_id != 349527:
+            continue
+
         ann_ids = coco.getAnnIds(imgIds = img_id)
         img_dict = coco.imgs[img_id]
         img_anns = coco.loadAnns(ann_ids)
@@ -54,17 +56,18 @@ def load_dataset(ann_path, images_dir, masks_dir, labels_dir):
                              img_anns[p]["bbox"][1] + img_anns[p]["bbox"][3] / 2]
 
             # skip this person if the distance to existing person is too small
-            flag = 0
-            for pc in prev_center:
-                a = np.expand_dims(pc[:2], axis=0)
-                b = np.expand_dims(person_center, axis=0)
-                dist = cdist(a, b)[0]
-                if dist < pc[2] * 0.3:
-                    flag = 1
-                    continue
+            if cfg.skip_adj == True:
+                flag = 0
+                for pc in prev_center:
+                    a = np.expand_dims(pc[:2], axis=0)
+                    b = np.expand_dims(person_center, axis=0)
+                    dist = cdist(a, b)[0]
+                    if dist < pc[2] * 0.3:
+                        flag = 1
+                        continue
 
-            if flag == 1:
-                continue
+                if flag == 1:
+                    continue
 
             pers["objpos"] = person_center
             pers["bbox"] = img_anns[p]["bbox"]
@@ -93,7 +96,7 @@ def load_dataset(ann_path, images_dir, masks_dir, labels_dir):
 
             pers["joint"] = transform_joints(joints)
 
-            pers["scale_provided"] = img_anns[p]["bbox"][3] / 368
+            pers["scale_provided"] = img_anns[p]["bbox"][3] / cfg.img_y
 
             persons.append(pers)
             prev_center.append(np.append(person_center, max(img_anns[p]["bbox"][2], img_anns[p]["bbox"][3])))
@@ -124,7 +127,7 @@ def load_dataset(ann_path, images_dir, masks_dir, labels_dir):
                 if p["num_keypoints"] <= 0:
                     mask_miss = np.bitwise_or(mask, mask_miss)
 
-            if flag<1:
+            if flag < 1:
                 mask_miss = np.logical_not(mask_miss)
             elif flag == 1:
                 mask_miss = np.logical_not(np.bitwise_or(mask_miss, mask_crowd))

diff --git a/reader.py b/reader.py
@@ -2,7 +2,7 @@
 import pickle
 import os
 import sys
-sys.path.insert(1, '../coco/PythonAPI/')
+sys.path.insert(1, '../coco/cocoapi/PythonAPI/')
 from pycocotools.coco import COCO
 
 from tensorpack import *
@@ -56,7 +56,6 @@ def __init__(self, train_or_test, shuffle, debug=False):
         if debug == True:
             self.img_id_list = self.img_id_list[:8]
 
-
     def size(self):
         return len(self.img_id_list)
 
@@ -203,8 +202,12 @@ def augmentation_flip(self, img, mask, label):
         right_idxes = [2, 3, 4, 8, 9, 10, 14, 16]
         left_idxes =  [5, 6, 7, 11, 12, 13, 15, 17]
 
+        _, w, _ = img_aug.shape
+
         # need to exchange left joints with right joints
         for person in label["persons"]:
+            for joint_idx in range(cfg.ch_heats - 1):
+                person["joint"][joint_idx, 0] = w - 1 - person["joint"][joint_idx, 0]
             for idx, joint_idx_1 in enumerate(right_idxes):
                 joint_idx_2 = left_idxes[idx]
                 # exchange the joint_idx_1-th joint with the joint_idx_2-th joint
@@ -241,9 +244,8 @@ def get_data(self):
             img, mask, label = self.augmentation_crop(img, mask, label)
             img, mask, label = self.augmentation_flip(img, mask, label)
 
-            # convert to model input format
-            # raw_h, raw_w, _ = img.shape
-            # img = cv2.resize(img, (cfg.img_y, cfg.img_x))
+            raw_h, raw_w, _ = img.shape
+            img = cv2.resize(img, (cfg.img_y, cfg.img_x))
             mask = cv2.resize(mask, (cfg.grid_y, cfg.grid_x)) / 255
 
             # create blank heat map
@@ -259,8 +261,8 @@ def get_data(self):
                     if person_label['joint'][i, 2] > 1: # cropped or unlabeled
                         continue
 
-                    x_center = person_label['joint'][i, 0]
-                    y_center = person_label['joint'][i, 1]
+                    x_center = person_label['joint'][i, 0] * cfg.img_x / raw_w
+                    y_center = person_label['joint'][i, 1] * cfg.img_y / raw_h
                     for g_y in range(cfg.grid_y):
                         for g_x in range(cfg.grid_x):
                             x = start + g_x * cfg.stride
@@ -290,12 +292,12 @@ def get_data(self):
 
                 for person_label in label["persons"]:
                     # get keypoint coord in the label map
-                    limb_from = Point(x=person_label['joint'][limb_from_kp, 0] / 8,
-                                      y=person_label['joint'][limb_from_kp, 1] / 8)
+                    limb_from = Point(x=person_label['joint'][limb_from_kp, 0] * cfg.img_x / raw_w / 8,
+                                      y=person_label['joint'][limb_from_kp, 1] * cfg.img_y / raw_h / 8)
                     limb_from_v = person_label['joint'][limb_from_kp, 2]
 
-                    limb_to = Point(x=person_label['joint'][limb_to_kp, 0] / 8,
-                                    y=person_label['joint'][limb_to_kp, 1] / 8)
+                    limb_to = Point(x=person_label['joint'][limb_to_kp, 0] * cfg.img_x / raw_w / 8,
+                                    y=person_label['joint'][limb_to_kp, 1] * cfg.img_y / raw_h / 8)
                     limb_to_v = person_label['joint'][limb_to_kp, 2]
 
                     if limb_from_v > 1 or limb_to_v > 1: