bounding_box = {
'cam_00_10_30': bbox1,
'cam_01_10_30': bbox2,
'cam_02_10_30': bbox3,
'cam_03_10_30': bbox4
}
img_paths = os.listdir(params['test_img_directory'])
for img_path in img_paths:
img = cv2.imread(os.path.join(params['test_img_directory'], img_path))
bbox = bounding_box[img_path[0:12]]
size = max((bbox[2] - bbox[0]), (bbox[3] - bbox[1]))
img_crop = np.copy(img)[bbox[1]:bbox[1] + size, bbox[0]:bbox[0] + size]
img_resize = cv2.resize(img_crop, (256, 256))
#test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2RGB)
if predictJoints:
predictions = model.predictJoints(img_resize, mode='gpu')
print(' Predict on {}'.format(img_path))
print(
np.add(
np.asarray(predictions) * (bbox[1] - bbox[0]) / 256,
np.array([bbox[0], bbox[2]])))
show_prections(img_resize, predictions,
img_path[0:-4] + '_pred' + img_path[-4:])
else:
# output heatmap = 1*64 x 64 x outputDim
out_heatmap = np.squeeze(model.predictHM(img_resize))
for i in range(out_heatmap.shape[2]):
joint_hm = np.asarray(cv2.resize(255 * out_heatmap[:, :, i],
(256, 256)),
dtype=np.uint8)
joint_hm = cv2.applyColorMap(joint_hm, cv2.COLORMAP_JET)
keypt = (int(coord[0]), int(coord[1]))
text_loc = (keypt[0]+5, keypt[1]+7)
cv2.circle(img, keypt, 3, RED, -1)
cv2.putText(img, str(i), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5, RED, 1, cv2.LINE_AA)
np.savetxt(a.output_dir+filename[:-4]+'_pred.csv',
predictions , delimiter=",", fmt='%i')
# print(a.output_dir+filename+'_pred.csv')
cv2.imwrite(a.output_dir+filename[:-4]+'_pred.png',img)
# cv2.imshow('img', img)
# cv2.waitKey(0)
if __name__ == '__main__':
print('--Parsing Config File')
params = process_config('config.cfg')
from os import listdir
ImageFileNames=listdir(a.input_dir)
model = Inference(model=a.checkpoint)
for i in range(len(ImageFileNames)):
#ImageName = a.input_dir+'/'+ImageFileNames[i]
img = cv2.imread(os.path.join( a.input_dir, ImageFileNames[i]))
test_img=img
predictions = model.predictJoints(test_img, mode='gpu')
show_prections(test_img, predictions,ImageFileNames[i])
