def get_video(video_path, output_path):
cap = cv2.VideoCapture(video_path)
e = tf_pose.get_estimator(model="mobilenet_thin")
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = (width, height)
# フレームレート(1フレームの時間単位はミリ秒)の取得
frame_rate = int(cap.get(cv2.CAP_PROP_FPS))
# ライター作成
fmt = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
writer = cv2.VideoWriter(output_path, fmt, frame_rate, size)
if cap.isOpened() is False:
print("Error opening video stream or file")
while cap.isOpened():
ret, image = cap.read()
if ret:
humans = e.inference(image, upsample_size=4.0)
image = e.draw_humans(image, humans, imgcopy=False)
writer.write(image)
else:
break
writer.release()
cap.release()
cv2.destroyAllWindows()
def get_image(img_path, output_path):
img = common.read_imgfile(img_path, None, None)
e = tf_pose.get_estimator(model="mobilenet_thin")
humans = e.inference(img, upsample_size=4.0)
image = e.draw_humans(img, humans, imgcopy=False)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
cv2.imwrite(output_path, image)
print('fin')
def seq_to_inf(frames, save_path=None):
estimator = tf_pose.get_estimator(model="mobilenet_thin")
J = param.get_general_params()['n_joints']
skl = np.zeros([J, 2, len(frames)]) - 1
bbx = np.zeros([len(frames), 4]) - 1
for f, fr in enumerate(frames):
points = estimator.inference(fr,
resize_to_default=(432 > 0 and 368 > 0),
upsample_size=4.0)[0].body_parts
min_x, min_y = (+np.inf, +np.inf)
max_x, max_y = (-np.inf, -np.inf)
imw, imh = fr[:, :, 0].shape
for key in points:
if key < J:
# Get the coordinates of the bodypart.
x, y = ((points[key].x) * imh, (points[key].y) * imw)
min_x = np.minimum(min_x, x)
min_y = np.minimum(min_y, y)
max_x = np.maximum(max_x, x)
max_y = np.maximum(max_y, y)
skl[key, 0, f] = x
skl[key, 1, f] = y
# plt.plot(skl[:, 0], skl[:, 1], "o", c="red", markersize=2)
# # Plot bound box based on skeleton joints.
# plt.plot([min_x, max_x, max_x, min_x, min_x],
# [min_y, min_y, max_y, max_y, min_y], "-", c="yellow")
bbx[f, :] = [min_x, min_y, max_x - min_x, max_y - min_y]
info = {"data": {"X": skl[:J], "bbox": bbx}}
if save_path:
sio.savemat(save_path + ".mat", info)
return info
def __init__(self):
# Use default width and height
self.__estimator = tf_pose.get_estimator(_MODEL)
# coding: utf-8
# In[103]:
import tf_pose
import cv2
import numpy as np
import matplotlib.pyplot as plt
import os
import shutil
# In[104]:
e = tf_pose.get_estimator()
w = 210
h = 260
# In[109]:
def get_hand(hand, w, h, frame):
y = int(hand.y * h)
x = int(hand.x * w)
x_off = 0
y_off = int(x_off * 1.42)
if x < 46:
x = 46
if x > 210 - 46:
x = 210 - 46
if y < 66:
y = 66
if y > 260 - 66:
def seq_to_inf(seq_path, save_path=None):
if not save_path:
save_path = seq_path + "/../info/"
fname = seq_path.split("/")[-3:-2][0]
if not os.path.exists(save_path):
os.makedirs(save_path)
frames = np.array([
cv2.imread(seq_path + "/" + file)[:, :, ::-1]
for file in sorted(os.listdir(seq_path))
])
estimator = tf_pose.get_estimator(model="mobilenet_thin")
J = param.get_general_params()['n_joints']
skl = np.zeros([J, 2, len(frames)]) - 1
bbx = np.zeros([len(frames), 4]) - 1
for f, fr in enumerate(frames):
try: # Captured bug: TO-DO: Solve it!
points = estimator.inference(fr,
resize_to_default=(432 > 0
and 368 > 0),
upsample_size=4.0)[0].body_parts
min_x, min_y = (+np.inf, +np.inf)
max_x, max_y = (-np.inf, -np.inf)
imw, imh = fr[:, :, 0].shape
for key in points:
if key < J:
# Get the coordinates of the bodypart.
x, y = ((points[key].x) * imh, (points[key].y) * imw)
min_x = np.minimum(min_x, x)
min_y = np.minimum(min_y, y)
max_x = np.maximum(max_x, x)
max_y = np.maximum(max_y, y)
skl[key, 0, f] = x
skl[key, 1, f] = y
# plt.plot(skl[:, 0], skl[:, 1], "o", c="red", markersize=2)
# # Plot bound box based on skeleton joints.
# plt.plot([min_x, max_x, max_x, min_x, min_x],
# [min_y, min_y, max_y, max_y, min_y], "-", c="yellow")
bbx[f, :] = [min_x, min_y, max_x - min_x, max_y - min_y]
except:
continue
info = {"data": {"X": skl[:J], "bbox": bbx}}
sio.savemat(save_path + fname + ".mat", info)
return info