99 lines
2.6 KiB
Python
Executable File
99 lines
2.6 KiB
Python
Executable File
import numpy as np
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import cv2
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from rknn.api import RKNN
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from yolov3_utils import yolov3_post_process, draw, download_yolov3_weight
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GRID0 = 13
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GRID1 = 26
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GRID2 = 52
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LISTSIZE = 85
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SPAN = 3
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if __name__ == '__main__':
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MODEL_PATH = './yolov3.cfg'
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WEIGHT_PATH = './yolov3.weights'
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RKNN_MODEL_PATH = './yolov3_416.rknn'
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im_file = './dog_bike_car_416x416.jpg'
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DATASET = './dataset.txt'
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# Download yolov3.weight
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download_yolov3_weight(WEIGHT_PATH)
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# Create RKNN object
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rknn = RKNN(verbose=True)
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# Pre-process config
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print('--> Config model')
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rknn.config(mean_values=[0, 0, 0], std_values=[255, 255, 255])
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print('done')
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# Load model
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print('--> Loading model')
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ret = rknn.load_darknet(model=MODEL_PATH, weight=WEIGHT_PATH)
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if ret != 0:
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print('Load model failed!')
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exit(ret)
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print('done')
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# Build model
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print('--> Building model')
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ret = rknn.build(do_quantization=True, dataset=DATASET)
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if ret != 0:
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print('Build model failed!')
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exit(ret)
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print('done')
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# Export rknn model
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print('--> Export rknn model')
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ret = rknn.export_rknn(RKNN_MODEL_PATH)
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if ret != 0:
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print('Export rknn model failed!')
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exit(ret)
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print('done')
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# Set inputs
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img = cv2.imread(im_file)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# Init runtime environment
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print('--> Init runtime environment')
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ret = rknn.init_runtime()
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if ret != 0:
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print('Init runtime environment failed!')
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exit(ret)
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print('done')
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# Inference
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print('--> Running model')
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outputs = rknn.inference(inputs=[img])
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print('done')
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input0_data = outputs[0]
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np.save('./darknet_yolov3_416x416_0.npy', input0_data)
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input1_data = outputs[1]
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np.save('./darknet_yolov3_416x416_1.npy', input1_data)
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input2_data = outputs[2]
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np.save('./darknet_yolov3_416x416_2.npy', input1_data)
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input0_data = input0_data.reshape(SPAN, LISTSIZE, GRID0, GRID0)
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input1_data = input1_data.reshape(SPAN, LISTSIZE, GRID1, GRID1)
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input2_data = input2_data.reshape(SPAN, LISTSIZE, GRID2, GRID2)
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input_data = []
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input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
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input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
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input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
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boxes, classes, scores = yolov3_post_process(input_data)
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image = cv2.imread(im_file)
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if boxes is not None:
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draw(image, boxes, scores, classes)
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print('Save results to results.jpg!')
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cv2.imwrite('result.jpg', image)
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rknn.release()
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