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import os
import sys
import time
import cv2
import numpy as np
import pandas as pd
from rknnlite.api import RKNNLite
pd.set_option("display.max_rows", 1000)
dim = 448
thresh = 0.5
# 初始化RKNNLite
rknn_lite = RKNNLite(verbose=False)
# 加载RKNN模型
ret = rknn_lite.load_rknn("model.rknn")
if ret != 0:
print('加载RKNN模型失败')
exit(ret)
# 初始化运行时环境
ret = rknn_lite.init_runtime()
if ret != 0:
print('初始化运行时环境失败')
exit(ret)
label_names = pd.read_csv("selected_tags.csv")
target_img = "input.jpg" if len(sys.argv) < 2 else sys.argv[1]
try:
# 图像预处理
# 1. 读取图像并转RGB
img = cv2.imread(target_img)
if img is None:
print(f"无法读取图像: {target_img}")
exit(1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 2. 处理透明通道
if img.shape[2] == 4: # RGBA图像
img = img[:, :, :3] # 只保留RGB通道
elif len(img.shape) == 2: # 灰度图
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
# 3. 填充为正方形
h, w = img.shape[:2]
if h > w:
diff = h - w
pad_left = diff // 2
pad_right = diff - pad_left
img = cv2.copyMakeBorder(img, 0, 0, pad_left, pad_right,
cv2.BORDER_CONSTANT, value=(255, 255, 255))
elif w > h:
diff = w - h
pad_top = diff // 2
pad_bottom = diff - pad_top
img = cv2.copyMakeBorder(img, pad_top, pad_bottom, 0, 0,
cv2.BORDER_CONSTANT, value=(255, 255, 255))
# 4. resize到目标尺寸
img = cv2.resize(img, (dim, dim), interpolation=cv2.INTER_AREA)
# 5. 转换为float32并添加batch维度
img = img.astype(np.float32)
img = np.expand_dims(img, 0)
print(img.shape) # 1, 448, 448, 3
# NHWC -> NWCH
# img = np.transpose(img, (0, 2, 3, 1))
# 执行推理
# np.save("img.npy",img)
start = time.time()
outputs = rknn_lite.inference(inputs=[img], data_format="nhwc")
end = time.time()
print(f"Inference time: {end - start} seconds")
probs = outputs[0] # 获取第一个输出
print(probs.shape)
# 后处理
label_names["probs"] = probs[0]
found_tags = label_names[label_names["probs"] > thresh][["tag_id", "name", "probs"]]
print(found_tags)
finally:
# 释放资源
rknn_lite.release() |