print('--> Export RKNN model')
ret = rknn.export_rknn('./mobilenet_v2.rknn')
if ret != 0:
print('Export mobilenet_v2.rknn failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread('./cat_224x224.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[img])
show_outputs(outputs)
print('done')
# perf
print('--> Begin evaluate model performance')
perf_results = rknn.eval_perf(inputs=[img])
print('done')
rknn.release()
iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1,
xmax1, ymax1)
if iou >= 0.45:
candidateBox[0][j] = -1
# Draw result
for i in range(0, vaildCnt):
if candidateBox[0][i] == -1:
continue
n = candidateBox[0][i]
xmin = max(0.0, min(1.0, predictions[0][n][1])) * INPUT_SIZE
ymin = max(0.0, min(1.0, predictions[0][n][0])) * INPUT_SIZE
xmax = max(0.0, min(1.0, predictions[0][n][3])) * INPUT_SIZE
ymax = max(0.0, min(1.0, predictions[0][n][2])) * INPUT_SIZE
# print("%d @ (%d, %d) (%d, %d) score=%f" % (topClassScoreIndex, xmin, ymin, xmax, ymax, topClassScore))
cv2.rectangle(orig_img, (int(xmin), int(ymin)), (int(xmax), int(ymax)),
(random.random() * 255, random.random() * 255,
random.random() * 255), 3)
cv2.imwrite("out.jpg", orig_img)
# Evaluate Perf on Simulator
rknn.eval_perf(inputs=[img], is_print=True)
# Release RKNN Context
rknn.release()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[visible_input, infrared_input])
print('done')
print('inference result: ', outputs)
img = np.array(outputs[0])
print(img.shape)
print('img.min = ')
print(np.min(img))
#img=img-np.min(img)
print(np.max(img))
img = img / np.max(img) * 255
img = np.reshape(img, (INPUT_SIZE_WIDTH, INPUT_SIZE_HEIGHT))
print(img.shape)
print(visible_input.shape)
cv2.imwrite("out.jpg", img)
# Evaluate Perf on Simulator
rknn.eval_perf(inputs=[visible_input], is_print=True)
# Release RKNN Context
rknn.release()
def main():
with open(yaml_file, 'r') as F:
config = yaml.load(F)
# print('config is:')
# print(config)
model_type = config['running']['model_type']
print('model_type is {}'.format(model_type))
rknn = RKNN(verbose=True)
print('--> config model')
rknn.config(**config['config'])
print('done')
print('--> Loading model')
load_function = getattr(rknn, _model_load_dict[model_type])
ret = load_function(**config['parameters'][model_type])
if ret != 0:
print('Load mobilenet_v2 failed! Ret = {}'.format(ret))
exit(ret)
print('done')
####
# print('hybrid_quantization')
# ret = rknn.hybrid_quantization_step1(dataset=config['build']['dataset'])
if model_type != 'rknn':
print('--> Building model')
ret = rknn.build(**config['build'])
if ret != 0:
print('Build mobilenet_v2 failed!')
exit(ret)
else:
print('--> skip Building model step, cause the model is already rknn')
if config['running']['export'] is True:
print('--> Export RKNN model')
ret = rknn.export_rknn(**config['export_rknn'])
if ret != 0:
print('Init runtime environment failed')
exit(ret)
else:
print('--> skip Export model')
if (config['running']['inference'] is True) or (config['running']['eval_perf'] is True):
print('--> Init runtime environment')
ret = rknn.init_runtime(**config['init_runtime'])
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('--> load img')
img = cv2.imread(config['img']['path'])
print('img shape is {}'.format(img.shape))
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
inputs = [img]
if config['running']['inference'] is True:
print('--> Running model')
config['inference']['inputs'] = inputs
#print(config['inference'])
outputs = rknn.inference(inputs)
#outputs = rknn.inference(config['inference'])
print('len of output {}'.format(len(outputs)))
print('outputs[0] shape is {}'.format(outputs[0].shape))
print(outputs[0][0][0:2])
else:
print('--> skip inference')
if config['running']['eval_perf'] is True:
print('--> Begin evaluate model performance')
config['inference']['inputs'] = inputs
perf_results = rknn.eval_perf(inputs=[img])
else:
print('--> skip eval_perf')
else:
print('--> skip inference')
print('--> skip eval_perf')
