[chore] exclude paddleocr directory from pre-commit hooks
This commit is contained in:
三洋三洋
226
texteller/models/det_model/inference.py
Normal file
226
texteller/models/det_model/inference.py
Normal file
@@ -0,0 +1,226 @@
|
||||
import os
|
||||
import time
|
||||
import yaml
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
from tqdm import tqdm
|
||||
from typing import List
|
||||
from .preprocess import Compose
|
||||
from .Bbox import Bbox
|
||||
|
||||
|
||||
# Global dictionary
|
||||
SUPPORT_MODELS = {
|
||||
'YOLO',
|
||||
'PPYOLOE',
|
||||
'RCNN',
|
||||
'SSD',
|
||||
'Face',
|
||||
'FCOS',
|
||||
'SOLOv2',
|
||||
'TTFNet',
|
||||
'S2ANet',
|
||||
'JDE',
|
||||
'FairMOT',
|
||||
'DeepSORT',
|
||||
'GFL',
|
||||
'PicoDet',
|
||||
'CenterNet',
|
||||
'TOOD',
|
||||
'RetinaNet',
|
||||
'StrongBaseline',
|
||||
'STGCN',
|
||||
'YOLOX',
|
||||
'HRNet',
|
||||
'DETR',
|
||||
}
|
||||
|
||||
|
||||
class PredictConfig(object):
|
||||
"""set config of preprocess, postprocess and visualize
|
||||
Args:
|
||||
infer_config (str): path of infer_cfg.yml
|
||||
"""
|
||||
|
||||
def __init__(self, infer_config):
|
||||
# parsing Yaml config for Preprocess
|
||||
with open(infer_config) as f:
|
||||
yml_conf = yaml.safe_load(f)
|
||||
self.check_model(yml_conf)
|
||||
self.arch = yml_conf['arch']
|
||||
self.preprocess_infos = yml_conf['Preprocess']
|
||||
self.min_subgraph_size = yml_conf['min_subgraph_size']
|
||||
self.label_list = yml_conf['label_list']
|
||||
self.use_dynamic_shape = yml_conf['use_dynamic_shape']
|
||||
self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
|
||||
self.mask = yml_conf.get("mask", False)
|
||||
self.tracker = yml_conf.get("tracker", None)
|
||||
self.nms = yml_conf.get("NMS", None)
|
||||
self.fpn_stride = yml_conf.get("fpn_stride", None)
|
||||
|
||||
color_pool = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
|
||||
self.colors = {
|
||||
label: color_pool[i % len(color_pool)] for i, label in enumerate(self.label_list)
|
||||
}
|
||||
|
||||
if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
|
||||
print('The RCNN export model is used for ONNX and it only supports batch_size = 1')
|
||||
self.print_config()
|
||||
|
||||
def check_model(self, yml_conf):
|
||||
"""
|
||||
Raises:
|
||||
ValueError: loaded model not in supported model type
|
||||
"""
|
||||
for support_model in SUPPORT_MODELS:
|
||||
if support_model in yml_conf['arch']:
|
||||
return True
|
||||
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf['arch'], SUPPORT_MODELS))
|
||||
|
||||
def print_config(self):
|
||||
print('----------- Model Configuration -----------')
|
||||
print('%s: %s' % ('Model Arch', self.arch))
|
||||
print('%s: ' % ('Transform Order'))
|
||||
for op_info in self.preprocess_infos:
|
||||
print('--%s: %s' % ('transform op', op_info['type']))
|
||||
print('--------------------------------------------')
|
||||
|
||||
|
||||
def draw_bbox(image, outputs, infer_config):
|
||||
for output in outputs:
|
||||
cls_id, score, xmin, ymin, xmax, ymax = output
|
||||
if score > infer_config.draw_threshold:
|
||||
label = infer_config.label_list[int(cls_id)]
|
||||
color = infer_config.colors[label]
|
||||
cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), color, 2)
|
||||
cv2.putText(
|
||||
image,
|
||||
"{}: {:.2f}".format(label, score),
|
||||
(int(xmin), int(ymin - 5)),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
color,
|
||||
2,
|
||||
)
|
||||
return image
|
||||
|
||||
|
||||
def predict_image(imgsave_dir, infer_config, predictor, img_list):
|
||||
# load preprocess transforms
|
||||
transforms = Compose(infer_config.preprocess_infos)
|
||||
errImgList = []
|
||||
|
||||
# Check and create subimg_save_dir if not exist
|
||||
subimg_save_dir = os.path.join(imgsave_dir, 'subimages')
|
||||
os.makedirs(subimg_save_dir, exist_ok=True)
|
||||
|
||||
first_image_skipped = False
|
||||
total_time = 0
|
||||
num_images = 0
|
||||
# predict image
|
||||
for img_path in tqdm(img_list):
|
||||
img = cv2.imread(img_path)
|
||||
if img is None:
|
||||
print(f"Warning: Could not read image {img_path}. Skipping...")
|
||||
errImgList.append(img_path)
|
||||
continue
|
||||
|
||||
inputs = transforms(img_path)
|
||||
inputs_name = [var.name for var in predictor.get_inputs()]
|
||||
inputs = {k: inputs[k][None,] for k in inputs_name}
|
||||
|
||||
# Start timing
|
||||
start_time = time.time()
|
||||
|
||||
outputs = predictor.run(output_names=None, input_feed=inputs)
|
||||
|
||||
# Stop timing
|
||||
end_time = time.time()
|
||||
inference_time = end_time - start_time
|
||||
if not first_image_skipped:
|
||||
first_image_skipped = True
|
||||
else:
|
||||
total_time += inference_time
|
||||
num_images += 1
|
||||
print(
|
||||
f"ONNXRuntime predict time for {os.path.basename(img_path)}: {inference_time:.4f} seconds"
|
||||
)
|
||||
|
||||
print("ONNXRuntime predict: ")
|
||||
if infer_config.arch in ["HRNet"]:
|
||||
print(np.array(outputs[0]))
|
||||
else:
|
||||
bboxes = np.array(outputs[0])
|
||||
for bbox in bboxes:
|
||||
if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
|
||||
print(f"{int(bbox[0])} {bbox[1]} " f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
|
||||
|
||||
# Save the subimages (crop from the original image)
|
||||
subimg_counter = 1
|
||||
for output in np.array(outputs[0]):
|
||||
cls_id, score, xmin, ymin, xmax, ymax = output
|
||||
if score > infer_config.draw_threshold:
|
||||
label = infer_config.label_list[int(cls_id)]
|
||||
subimg = img[int(max(ymin, 0)) : int(ymax), int(max(xmin, 0)) : int(xmax)]
|
||||
if len(subimg) == 0:
|
||||
continue
|
||||
|
||||
subimg_filename = f"{os.path.splitext(os.path.basename(img_path))[0]}_{label}_{xmin:.2f}_{ymin:.2f}_{xmax:.2f}_{ymax:.2f}.jpg"
|
||||
subimg_path = os.path.join(subimg_save_dir, subimg_filename)
|
||||
cv2.imwrite(subimg_path, subimg)
|
||||
subimg_counter += 1
|
||||
|
||||
# Draw bounding boxes and save the image with bounding boxes
|
||||
img_with_mask = img.copy()
|
||||
for output in np.array(outputs[0]):
|
||||
cls_id, score, xmin, ymin, xmax, ymax = output
|
||||
if score > infer_config.draw_threshold:
|
||||
cv2.rectangle(
|
||||
img_with_mask,
|
||||
(int(xmin), int(ymin)),
|
||||
(int(xmax), int(ymax)),
|
||||
(255, 255, 255),
|
||||
-1,
|
||||
) # 盖白
|
||||
|
||||
img_with_bbox = draw_bbox(img, np.array(outputs[0]), infer_config)
|
||||
|
||||
output_dir = imgsave_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
draw_box_dir = os.path.join(output_dir, 'draw_box')
|
||||
mask_white_dir = os.path.join(output_dir, 'mask_white')
|
||||
os.makedirs(draw_box_dir, exist_ok=True)
|
||||
os.makedirs(mask_white_dir, exist_ok=True)
|
||||
|
||||
output_file_mask = os.path.join(mask_white_dir, os.path.basename(img_path))
|
||||
output_file_bbox = os.path.join(draw_box_dir, os.path.basename(img_path))
|
||||
cv2.imwrite(output_file_mask, img_with_mask)
|
||||
cv2.imwrite(output_file_bbox, img_with_bbox)
|
||||
|
||||
avg_time_per_image = total_time / num_images if num_images > 0 else 0
|
||||
print(f"Total inference time for {num_images} images: {total_time:.4f} seconds")
|
||||
print(f"Average time per image: {avg_time_per_image:.4f} seconds")
|
||||
print("ErrorImgs:")
|
||||
print(errImgList)
|
||||
|
||||
|
||||
def predict(img_path: str, predictor, infer_config) -> List[Bbox]:
|
||||
transforms = Compose(infer_config.preprocess_infos)
|
||||
inputs = transforms(img_path)
|
||||
inputs_name = [var.name for var in predictor.get_inputs()]
|
||||
inputs = {k: inputs[k][None,] for k in inputs_name}
|
||||
|
||||
outputs = predictor.run(output_names=None, input_feed=inputs)[0]
|
||||
res = []
|
||||
for output in outputs:
|
||||
cls_name = infer_config.label_list[int(output[0])]
|
||||
score = output[1]
|
||||
xmin = int(max(output[2], 0))
|
||||
ymin = int(max(output[3], 0))
|
||||
xmax = int(output[4])
|
||||
ymax = int(output[5])
|
||||
if score > infer_config.draw_threshold:
|
||||
res.append(Bbox(xmin, ymin, ymax - ymin, xmax - xmin, cls_name, score))
|
||||
|
||||
return res
|
||||
Reference in New Issue
Block a user