rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/test.py

import numpy as np

import re
import math
import random
import cv2

from rknn.api import RKNN

INPUT_SIZE = 300

NUM_RESULTS = 1917
NUM_CLASSES = 91

Y_SCALE = 10.0
X_SCALE = 10.0
H_SCALE = 5.0
W_SCALE = 5.0


def expit(x):
    return 1. / (1. + math.exp(-x))


def unexpit(y):
    return -1.0 * math.log((1.0 / y) - 1.0)


def CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1):
    w = max(0.0, min(xmax0, xmax1) - max(xmin0, xmin1))
    h = max(0.0, min(ymax0, ymax1) - max(ymin0, ymin1))
    i = w * h
    u = (xmax0 - xmin0) * (ymax0 - ymin0) + (xmax1 - xmin1) * (ymax1 - ymin1) - i

    if u <= 0.0:
        return 0.0

    return i / u


def load_box_priors():
    box_priors_ = []
    fp = open('./box_priors.txt', 'r')
    ls = fp.readlines()
    for s in ls:
        aList = re.findall('([-+]?\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?', s)
        for ss in aList:
            aNum = float((ss[0]+ss[2]))
            box_priors_.append(aNum)
    fp.close()

    box_priors = np.array(box_priors_)
    box_priors = box_priors.reshape(4, NUM_RESULTS)

    return box_priors


if __name__ == '__main__':

    # Create RKNN object
    rknn = RKNN(verbose=True)

    # Pre-process config
    print('--> Config model')
    rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5])
    print('done')

    # Load model
    print('--> Loading model')
    ret = rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v1_coco_2017_11_17.pb',
                               inputs=['Preprocessor/sub'],
                               outputs=['concat', 'concat_1'],
                               input_size_list=[[1, INPUT_SIZE, INPUT_SIZE, 3]])
    if ret != 0:
        print('Load model failed!')
        exit(ret)
    print('done')

    # Build Model
    print('--> Building model')
    ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
    if ret != 0:
        print('Build model failed!')
        exit(ret)
    print('done')

    # Export rknn model
    print('--> Export rknn model')
    ret = rknn.export_rknn('./ssd_mobilenet_v1_coco.rknn')
    if ret != 0:
        print('Export rknn model failed!')
        exit(ret)
    print('done')

    # Set inputs
    orig_img = cv2.imread('./road.bmp')
    img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (INPUT_SIZE, INPUT_SIZE), interpolation=cv2.INTER_CUBIC)

    # Init runtime environment
    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])
    print('done')

    predictions = outputs[0].reshape((1, NUM_RESULTS, 4))
    np.save('./tensorflow_ssd_mobilenet_v1_0.npy', outputs[0])
    outputClasses = outputs[1].reshape((1, NUM_RESULTS, NUM_CLASSES))
    np.save('./tensorflow_ssd_mobilenet_v1_1.npy', outputs[0])
    candidateBox = np.zeros([2, NUM_RESULTS], dtype=int)
    vaildCnt = 0

    box_priors = load_box_priors()

    # Post Process
    # got valid candidate box
    for i in range(0, NUM_RESULTS):
        topClassScore = -1000
        topClassScoreIndex = -1

        # Skip the first catch-all class.
        for j in range(1, NUM_CLASSES):
            score = expit(outputClasses[0][i][j])

            if score > topClassScore:
                topClassScoreIndex = j
                topClassScore = score

        if topClassScore > 0.4:
            candidateBox[0][vaildCnt] = i
            candidateBox[1][vaildCnt] = topClassScoreIndex
            vaildCnt += 1

    # calc position
    for i in range(0, vaildCnt):
        if candidateBox[0][i] == -1:
            continue

        n = candidateBox[0][i]
        ycenter = predictions[0][n][0] / Y_SCALE * box_priors[2][n] + box_priors[0][n]
        xcenter = predictions[0][n][1] / X_SCALE * box_priors[3][n] + box_priors[1][n]
        h = math.exp(predictions[0][n][2] / H_SCALE) * box_priors[2][n]
        w = math.exp(predictions[0][n][3] / W_SCALE) * box_priors[3][n]

        ymin = ycenter - h / 2.
        xmin = xcenter - w / 2.
        ymax = ycenter + h / 2.
        xmax = xcenter + w / 2.

        predictions[0][n][0] = ymin
        predictions[0][n][1] = xmin
        predictions[0][n][2] = ymax
        predictions[0][n][3] = xmax

    # NMS
    for i in range(0, vaildCnt):
        if candidateBox[0][i] == -1:
            continue

        n = candidateBox[0][i]
        xmin0 = predictions[0][n][1]
        ymin0 = predictions[0][n][0]
        xmax0 = predictions[0][n][3]
        ymax0 = predictions[0][n][2]

        for j in range(i+1, vaildCnt):
            m = candidateBox[0][j]

            if m == -1:
                continue

            xmin1 = predictions[0][m][1]
            ymin1 = predictions[0][m][0]
            xmax1 = predictions[0][m][3]
            ymax1 = predictions[0][m][2]

            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)

    rknn.release()