146 lines
4.9 KiB
Python
146 lines
4.9 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：design_ensemble.py
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@Author ：周成融
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@Date ：2023/8/16 19:36:21
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@detail ：发起请求 获取推理结果
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"""
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import logging
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import cv2
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import mmcv
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import numpy as np
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import torch
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import tritonclient.http as httpclient
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from app.core.config import *
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"""
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keypoint
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预处理 推理 后处理
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"""
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def keypoint_preprocess(img_path):
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img = mmcv.imread(img_path)
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img_scale = (256, 256)
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h, w = img.shape[:2]
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img = cv2.resize(img, img_scale)
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w_scale = img_scale[0] / w
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h_scale = img_scale[1] / h
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img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
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preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
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return preprocessed_img, (w_scale, h_scale)
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# @ RunTime
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# 推理
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def get_keypoint_result(image, site):
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keypoint_result = None
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try:
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image, scale_factor = keypoint_preprocess(image)
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client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
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transformed_img = image.astype(np.float32)
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inputs = [httpclient.InferInput(f"input", transformed_img.shape, datatype="FP32")]
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inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
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outputs = [httpclient.InferRequestedOutput(f"output", binary_data=True)]
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results = client.infer(model_name=f"keypoint_{site}_ocrnet_hr18", inputs=inputs, outputs=outputs)
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inference_output = torch.from_numpy(results.as_numpy(f'output'))
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keypoint_result = keypoint_postprocess(inference_output, scale_factor)
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except Exception as e:
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logging.warning(f"get_keypoint_result : {e}")
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return keypoint_result
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def keypoint_postprocess(output, scale_factor):
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max_indices = torch.argmax(output.view(output.size(0), output.size(1), -1), dim=2).unsqueeze(dim=2)
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max_coords = torch.cat((max_indices / output.size(3), max_indices % output.size(3)), dim=2)
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segment_result = max_coords.numpy()
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scale_factor = [1 / x for x in scale_factor[::-1]]
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scale_matrix = np.diag(scale_factor)
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nan = np.isinf(scale_matrix)
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scale_matrix[nan] = 0
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return np.ceil(np.dot(segment_result, scale_matrix) * 4)
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"""
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seg
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预处理 推理 后处理
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"""
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# KNet
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def seg_preprocess(img_path):
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img = mmcv.imread(img_path)
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ori_shape = img.shape[:2]
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img_scale_w, img_scale_h = ori_shape
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if ori_shape[0] > 1024:
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img_scale_w = 1024
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if ori_shape[1] > 1024:
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img_scale_h = 1024
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# 如果图片size任意一边 大于 1024， 则会resize 成1024
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if ori_shape != (img_scale_w, img_scale_h):
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# mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
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img = cv2.resize(img, (img_scale_h, img_scale_w))
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# 扩充25的白边
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img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
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# img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
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preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
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return preprocessed_img, ori_shape
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# @ RunTime
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def get_seg_result(image_id, image):
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image, ori_shape = seg_preprocess(image)
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client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}")
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transformed_img = image.astype(np.float32)
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# 输入集
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inputs = [
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httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32")
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]
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inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
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# 输出集
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outputs = [
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httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
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]
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results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs)
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# 推理
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# 取结果
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inference_output1 = results.as_numpy(SEGMENTATION['output'])
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seg_result = seg_postprocess(int(image_id), inference_output1, ori_shape)
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return seg_result
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# no cache
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def seg_postprocess(image_id, output, ori_shape):
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seg_logit = cv2.resize(output[0][0].astype(np.uint8), (ori_shape[1] + 50, ori_shape[0] + 50))
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seg_logit = seg_logit[25: - 25, 25: - 25]
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return seg_logit
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def key_point_show(image_path, key_point_result=None):
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img = cv2.imread(image_path)
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points_list = key_point_result
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point_size = 1
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point_color = (0, 0, 255) # BGR
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thickness = 4 # 可以为 0 、4、8
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for point in points_list:
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cv2.circle(img, point[::-1], point_size, point_color, thickness)
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cv2.imshow("0", img)
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cv2.waitKey(0)
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if __name__ == '__main__':
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image = cv2.imread("9070101c-e5be-49b5-9602-4113a968969b.png")
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a = get_keypoint_result(image, "up")
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new_list = []
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print(list)
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for i in a[0]:
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new_list.append((int(i[0]), int(i[1])))
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key_point_show("9070101c-e5be-49b5-9602-4113a968969b.png", new_list)
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# a = get_seg_result(1, image)
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print(a)
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