def detec_hum(self, image): # arg: numpy array with shape of (, , 3)
# resize the image manually to the fixed model input size
# shape_orig = image.shape
image = cv2.resize(image, (self.C.size_test[1], self.C.size_test[0]))
x_rcnn = format_img(image, self.C)
Y = self.model.predict(x_rcnn)
boxes = bbox_process.parse_det_offset(Y, self.C, score=0.1,down=4)
return boxes[boxes[:, 4]>0.3] if len(boxes)>0 else []
def csp_process():
try:
req = json.loads(request.body.read())
score = req['score']
img = np.array(req['img'], np.uint8)
assert (img.shape[0], img.shape[1]) == C.size_test
except (TypeError, AssertionError, IndexError):
return 'null'
x_rcnn = format_img(img, C)
Y = model.predict(x_rcnn)
if C.offset:
boxes = bbox_process.parse_det_offset(Y, C, score=score, down=4)
else:
boxes = bbox_process.parse_det(Y,
C,
score=score,
down=4,
scale=C.scale)
boxes[:, [0, 1, 2, 3, 4]] = boxes[:, [1, 0, 3, 2, 4]]
return json.dumps(boxes.tolist())
print('load weights from {}'.format(weight1))
model.load_weights(weight1, by_name=True)
res_all = []
start_time = time.time()
for f in range(num_imgs):
filepath = val_data[f]['filepath']
images_dir_name = 'images{}/'.format(exp_name if 'base' not in
exp_name else '')
filepath = filepath.replace('images/', images_dir_name)
img = cv2.imread(filepath)
x_rcnn = format_img(img, C)
Y = model.predict(x_rcnn)
if C.offset:
boxes = bbox_process.parse_det_offset(Y, C, score=0.1, down=4)
else:
boxes = bbox_process.parse_det(Y, C, score=0.1, down=4, scale=C.scale)
if len(boxes) > 0:
f_res = np.repeat(f + 1, len(boxes), axis=0).reshape((-1, 1))
boxes[:, [2, 3]] -= boxes[:, [0, 1]]
res_all += np.concatenate((f_res, boxes), axis=-1).tolist()
np.savetxt(res_file_txt, np.array(res_all), fmt='%6f')
print(time.time() - start_time)
dt_path = res_path
json_path = convert_file(res_file_txt)
annFile_test = '/home/vobecant/PhD/CSP/eval_city/val_gt.json'
resFile = os.path.join(dt_path, 'val_dt.json')
respath = os.path.join(dt_path, 'results_testSet.txt')
