Spaces:
Runtime error
Runtime error
File size: 3,609 Bytes
938e515 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
import cv2
import json
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from skimage.measure import label
from .model import handpose_model
from . import util
class Hand(object):
def __init__(self, model_path):
self.model = handpose_model()
if torch.cuda.is_available():
self.model = self.model.cuda()
print('cuda')
model_dict = util.transfer(self.model, torch.load(model_path))
self.model.load_state_dict(model_dict)
self.model.eval()
def __call__(self, oriImgRaw):
scale_search = [0.5, 1.0, 1.5, 2.0]
# scale_search = [0.5]
boxsize = 368
stride = 8
padValue = 128
thre = 0.05
multiplier = [x * boxsize for x in scale_search]
wsize = 128
heatmap_avg = np.zeros((wsize, wsize, 22))
Hr, Wr, Cr = oriImgRaw.shape
oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8)
for m in range(len(multiplier)):
scale = multiplier[m]
imageToTest = util.smart_resize(oriImg, (scale, scale))
imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
im = np.ascontiguousarray(im)
data = torch.from_numpy(im).float()
if torch.cuda.is_available():
data = data.cuda()
with torch.no_grad():
output = self.model(data).cpu().numpy()
# output_path = "/home/aigc/ProjectVTON/WebDemo/onnx_models/hand_estimation.onnx"
# torch.onnx.export(self.model, data, output_path, export_params=True,
# opset_version=11,
# do_constant_folding=True)
# extract outputs, resize, and remove padding
heatmap = np.transpose(np.squeeze(output), (1, 2, 0)) # output 1 is heatmaps
heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
heatmap = util.smart_resize(heatmap, (wsize, wsize))
heatmap_avg += heatmap / len(multiplier)
all_peaks = []
for part in range(21):
map_ori = heatmap_avg[:, :, part]
one_heatmap = gaussian_filter(map_ori, sigma=3)
binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
if np.sum(binary) == 0:
all_peaks.append([0, 0])
continue
label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
label_img[label_img != max_index] = 0
map_ori[label_img == 0] = 0
y, x = util.npmax(map_ori)
y = int(float(y) * float(Hr) / float(wsize))
x = int(float(x) * float(Wr) / float(wsize))
all_peaks.append([x, y])
return np.array(all_peaks)
if __name__ == "__main__":
hand_estimation = Hand('../model/hand_pose_model.pth')
# test_image = '../images/hand.jpg'
test_image = '../images/hand.jpg'
oriImg = cv2.imread(test_image) # B,G,R order
peaks = hand_estimation(oriImg)
canvas = util.draw_handpose(oriImg, peaks, True)
cv2.imshow('', canvas)
cv2.waitKey(0) |