Fix Bug For python 3

camera_calibration.py

@@ -0,0 +1,66 @@
+import numpy as np
+import cv2
+
+# termination criteria
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
+objp = np.zeros((6*7, 3), np.float32)
+objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
+# Arrays to store object points and image points from all the images.
+objpoints = []  # 3d point in real world space
+imgpoints = []  # 2d points in image plane.
+
+w = 1280
+h = 720
+
+target_size = 720
+
+s_x = (w//2-target_size//2)
+e_x = (w//2+target_size//2)
+s_y = 0
+e_y = target_size
+
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
+
+while cap.isOpened():
+    success, img = cap.read()
+    if not success:
+        print("Ignoring empty camera frame.")
+        # If loading a video, use 'break' instead of 'continue'.
+        continue
+
+    img = img[s_y:e_y, s_x:e_x]
+    img = cv2.resize(img, (1080, 1080))
+    # print(img.shape)
+
+    width = img.shape[0]
+    height = img.shape[1]
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # Find the chess board corners
+    ret, corners = cv2.findChessboardCorners(gray, (7, 6), None)
+    # If found, add object points, image points (after refining them)
+    if ret == True:
+        objpoints.append(objp)
+        corners2 = cv2.cornerSubPix(
+            gray, corners, (11, 11), (-1, -1), criteria)
+        imgpoints.append(corners)
+        # Draw and display the corners
+        cv2.drawChessboardCorners(img, (7, 6), corners2, ret)
+    cv2.imshow('img', img)
+
+    if cv2.waitKey(5) & 0xFF == 27:
+        break
+
+cv2.destroyAllWindows()
+
+ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
+    objpoints, imgpoints, gray.shape[::-1], None, None)
+
+print(f"width:{width}")
+print(f"height:{height}")
+print(f"ret:{ret}")
+print(f"mtx:{mtx}")
+print(f"dist:{dist}")

environment.yml

@@ -0,0 +1,42 @@
+name: 360_vdo_reconstruck
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=4.5
+  - ca-certificates=2022.4.26
+  - certifi=2021.10.8
+  - ld_impl_linux-64=2.35.1
+  - libffi=3.3
+  - libgcc-ng=9.3.0
+  - libgomp=9.3.0
+  - libstdcxx-ng=9.3.0
+  - ncurses=6.3
+  - openssl=1.1.1o
+  - pip=21.2.2
+  - python=3.7.13
+  - readline=8.1.2
+  - setuptools=61.2.0
+  - sqlite=3.38.3
+  - tk=8.6.11
+  - wheel=0.37.1
+  - xz=5.2.5
+  - zlib=1.2.12
+  - pip:
+    - cached-property==1.5.2
+    - chumpy==0.70
+    - cycler==0.11.0
+    - cython==0.29.28
+    - h5py==3.6.0
+    - kiwisolver==1.4.2
+    - matplotlib==2.2.5
+    - numpy==1.21.6
+    - opencv-python==4.5.5.64
+    - opendr==0.77
+    - pyparsing==3.0.8
+    - python-dateutil==2.8.2
+    - pytz==2022.1
+    - scipy==1.7.3
+    - six==1.16.0
+    - tqdm==4.64.0
+    - typing-extensions==4.2.0

hdf5_read.py

@@ -0,0 +1,17 @@
+import h5py
+import numpy as np
+filename = "reconstructed_poses_noratap.hdf5"
+
+with h5py.File(filename, "r") as f:
+    # List all groups
+    print("Keys: %s" % f.keys())
+    a_group_key = list(f.keys())[1]
+
+    # Get the data
+    data = list(f[a_group_key])
+    pose_data = data[0].reshape((24, 3))
+
+    for i in range(0, 24):
+        print(f"{i}:{np.degrees(pose_data[i])}")
+
+    print(len(data[0]))

lib/rays.py

@@ -27,8 +27,14 @@ def unpose_and_select_rays(rays, Vi, smpl, rn_b, rn_m):
     v_ids = visible_boundary_edge_verts(rn_b, rn_m)
     verts = smpl.r[v_ids]
 
+    print(rn_b)
+    print(rn_m)
+    print(v_ids)
+    print(verts)
+
     n, m = plucker(rays)
-    dist = np.linalg.norm(np.cross(verts.reshape(-1, 1, 3), n, axisa=2, axisb=1) - m, axis=2)
+    dist = np.linalg.norm(np.cross(verts.reshape(-1, 1, 3),
+                          n, axisa=2, axisb=1) - m, axis=2)
 
     ray_matches = np.argmin(dist, axis=0)
     vert_matches = np.argmin(dist, axis=1)
@@ -38,27 +44,35 @@ def unpose_and_select_rays(rays, Vi, smpl, rn_b, rn_m):
     M = Vi[:, :, v_ids]
     T = smpl.v_posevariation[v_ids].r
 
-    tmp0 = M[:, :, ray_matches] * np.hstack((rays[:, 0], np.ones((rays.shape[0], 1)))).T.reshape(1, 4, -1)
-    tmp1 = M[:, :, ray_matches] * np.hstack((rays[:, 1], np.ones((rays.shape[0], 1)))).T.reshape(1, 4, -1)
+    tmp0 = M[:, :, ray_matches] * \
+        np.hstack((rays[:, 0], np.ones((rays.shape[0], 1)))
+                  ).T.reshape(1, 4, -1)
+    tmp1 = M[:, :, ray_matches] * \
+        np.hstack((rays[:, 1], np.ones((rays.shape[0], 1)))
+                  ).T.reshape(1, 4, -1)
 
     rays_u_r[:, 0] = np.sum(tmp0, axis=1).T[:, :3] - T[ray_matches]
     rays_u_r[:, 1] = np.sum(tmp1, axis=1).T[:, :3] - T[ray_matches]
 
     rays_u_v = np.zeros_like(rays[vert_matches])
 
-    tmp0 = M * np.hstack((rays[vert_matches, 0], np.ones((verts.shape[0], 1)))).T.reshape(1, 4, -1)
-    tmp1 = M * np.hstack((rays[vert_matches, 1], np.ones((verts.shape[0], 1)))).T.reshape(1, 4, -1)
+    tmp0 = M * np.hstack((rays[vert_matches, 0],
+                         np.ones((verts.shape[0], 1)))).T.reshape(1, 4, -1)
+    tmp1 = M * np.hstack((rays[vert_matches, 1],
+                         np.ones((verts.shape[0], 1)))).T.reshape(1, 4, -1)
 
     rays_u_v[:, 0] = np.sum(tmp0, axis=1).T[:, :3] - T
     rays_u_v[:, 1] = np.sum(tmp1, axis=1).T[:, :3] - T
 
-    valid_rays = dist[np.vstack((ray_matches, range(dist.shape[1]))).tolist()] < 0.12
-    valid_verts = dist[np.vstack((range(dist.shape[0]), vert_matches)).tolist()] < 0.03
+    valid_rays = dist[np.vstack(
+        (ray_matches, range(dist.shape[1]))).tolist()] < 0.12
+    valid_verts = dist[np.vstack(
+        (range(dist.shape[0]), vert_matches)).tolist()] < 0.03
 
     ray_matches = ray_matches[valid_rays]
 
     return np.concatenate((v_ids[ray_matches], v_ids[valid_verts])), \
-           np.concatenate((rays_u_r[valid_rays], rays_u_v[valid_verts]))
+        np.concatenate((rays_u_r[valid_rays], rays_u_v[valid_verts]))
 
 
 def rays_from_points(points, camera):
@@ -73,9 +87,12 @@ def rays_from_points(points, camera):
 def rays_from_silh(mask, camera):
 
     if cv2.__version__[0] == '2':
-        contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        contours, _ = cv2.findContours(
+            mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
     else:
-        _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        #_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        contours, _ = cv2.findContours(
+            mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
 
     silh = np.zeros_like(mask)
 
@@ -92,7 +109,8 @@ def ray_objective(f, sigma, base_smpl, camera, vis_rn_b, vis_rn_m):
     base_smpl.pose[:] = f.pose
     camera.t[:] = f.trans
 
-    f.v_ids, f.rays_u = unpose_and_select_rays(f.rays, f.Vi, base_smpl, vis_rn_b, vis_rn_m)
+    f.v_ids, f.rays_u = unpose_and_select_rays(
+        f.rays, f.Vi, base_smpl, vis_rn_b, vis_rn_m)
     f.verts = base_smpl.v_shaped_personal[f.v_ids]
     f.dist = distance_function(f.rays_u, f.verts)
 

mask_numpy2pic.py

@@ -0,0 +1,30 @@
+import numpy as np
+import cv2
+import h5py
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('input', type=str)
+parser.add_argument('output', type=str)
+
+args = parser.parse_args()
+
+input_file = args.input
+output_folder = args.output
+
+
+with h5py.File(input_file, "r") as f:
+    # List all groups
+    print("Keys: %s" % f.keys())
+    a_group_key = list(f.keys())[0]
+
+    # Get the data
+    data = list(f[a_group_key])
+    for frame, mask in enumerate(data):
+        print(frame)
+        print(mask.shape)
+        white_img = np.ones((1080, 1080), dtype='uint8')*255
+        mask = cv2.bitwise_and(white_img, white_img, mask=mask)
+        print(mask.dtype)
+        #mask *= 255
+        cv2.imwrite(f"{output_folder}/{frame}.png", mask)

mask_person_by_bg_subtrack.py

@@ -0,0 +1,72 @@
+import cv2
+import numpy as np
+
+vdo_file = "E:/CMU Med Data Analysis/3d_reconstruck/dataset/input/Noratap/noratap_360.mp4"
+bg_file = "E:/CMU Med Data Analysis/3d_reconstruck/dataset/input/Noratap/bg.jpg"
+
+mask_out_path = "E:/CMU Med Data Analysis/3d_reconstruck/dataset/input/Noratap/mask"
+
+# Set Up Vdo Write
+vdo_name = "E:/CMU Med Data Analysis/3d_reconstruck/dataset/input/Noratap/noratap_360_1080.avi"
+print("VDO Save ", vdo_name)
+vdo_out = cv2.VideoWriter(vdo_name, cv2.VideoWriter_fourcc(
+    *'DIVX'), 25, (1080, 1080))
+
+cap = cv2.VideoCapture(vdo_file)
+img_bg = cv2.imread(bg_file)
+
+w = img_bg.shape[0]
+h = img_bg.shape[1]
+
+target_size = 720
+
+s_x = (w//2-target_size//2) + 350
+e_x = (w//2+target_size//2) + 350
+s_y = 0
+e_y = target_size
+
+img_bg = img_bg[s_y:e_y, s_x:e_x]
+
+frame = 0
+
+if (cap.isOpened() == False):
+    print("Error opening video stream or file")
+
+while(cap.isOpened()):
+    ret, img = cap.read()
+    if ret == True:
+
+        img = img[s_y:e_y, s_x:e_x]
+
+        cv2.imshow('Img', img)
+
+        # bg Subtrack
+        img_mask = cv2.subtract(img_bg, img)
+        img_mask = cv2.cvtColor(img_mask, cv2.COLOR_BGR2GRAY)
+
+       # img_mask = cv2.equalizeHist(img_mask)
+
+        ret_th, img_mask = cv2.threshold(img_mask, 30, 255, cv2.THRESH_BINARY)
+
+        # img_mask = cv2.dilate(img_mask, np.ones((10, 10)))
+
+        cv2.imshow('Mask', img_mask)
+
+        img_mask = cv2.resize(img_mask, (1080, 1080))
+        img = cv2.resize(img, (1080, 1080))
+
+        # Save Img
+        cv2.imwrite(f"{mask_out_path}/{frame}.png", img_mask)
+        vdo_out.write(img)
+
+        frame += 1
+
+        if cv2.waitKey(25) & 0xFF == ord('q'):
+            break
+
+    else:
+        break
+
+vdo_out.release()
+cap.release()
+cv2.destroyAllWindows()

models/bodyparts.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python2
 # -*- coding: utf-8 -*-
 
-import cPickle as pkl
+import pickle as pkl
 import numpy as np
 
 _cache = None
@@ -12,7 +12,9 @@ def get_bodypart_vertex_ids():
 
     if _cache is None:
         with open('assets/bodyparts.pkl', 'rb') as fp:
-            _cache = pkl.load(fp)
+            u = pkl._Unpickler(fp)
+            u.encoding = 'latin1'
+            _cache = u.load()
 
     return _cache