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single_infer_optimize.py
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import argparse
import json
import math
import os
import sys
import time
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
sys.path.append("/root/VinMap")
import ast
import shutil
# classification
import mmcv
from classification.mmcls.apis import inference_model, init_model, show_result_pyplot
from classification.mmcls.apis.inference import data_infer
from classification.mmcls.utils import auto_select_device
# vocab_filtering
from eval import check, mapper
from mmcv import Config
#
from PaddleOCR.tools.infer.predict_det_our import readtext
from PIL import Image, ImageDraw, ImageFont
from tools.minimum_hull import minimum_bounding_rectangle
# segmentation
from tqdm import tqdm
from unidecode import unidecode
from vietocr.tool.config import Cfg
# from mmocr.utils.ocr import MMOCR
# recognition
from vietocr.tool.predictor import Predictor
def parse_args():
parser = argparse.ArgumentParser(description="Release-24.03.12")
parser.add_argument(
"-configCls",
"--configCls",
default="classification/configs/efficientnet/efficientnet-b4_8xb32_in1k.py",
help="Map Classification Configuration",
)
parser.add_argument(
"-cls_weights", "--cls_weights", default="../weight/effnetb4_vnmap.pth", help="Map Classification Weight"
)
parser.add_argument(
"-rec_weights", "--rec_weights", default="../weight/transformerocr_btc.pth", help="Text Recognition Weight"
)
parser.add_argument("--root", "--root", default="None", help="Root Workdirectory")
parser.add_argument("-input_images", "--input_images", default="None", help="Input images path")
parser.add_argument("-output_destination", "--output_destination", default="None", help="Output path")
parser.add_argument("-single_infer", "--single_infer", default="True", help="Type of single infering")
parser.add_argument(
"-single_infer_image", "--single_infer_image", default="None", help="Input path of single image"
)
parser.add_argument(
"-single_infer_path", "--single_infer_path", default="None", help="Output path of single infer"
)
args = parser.parse_args()
return args
def excecute(root, folder_path, image_folder_path):
print("Excecuting OCR")
os.makedirs(folder_path + "/" + "predicted")
for f in tqdm(os.listdir(image_folder_path)):
file_type = f.split(".")[1]
file_name = f.split(".")[0]
image = cv2.imread(os.path.join(image_folder_path, f))
ff = None
try:
ff = open(os.path.join(folder_path, "out_" + file_name + ".json"), "r")
except:
continue
all_data = json.load(ff)
boundary_results = all_data["boundary_result"]
results = []
ii = Image.fromarray(np.array(image))
draw = ImageDraw.Draw(ii)
for boundary_result in boundary_results:
np_image = np.array(image)
info = []
if boundary_result[-1] < 0.1:
continue
points = []
for i in range(0, len(boundary_result) - 2, 2):
points.append(tuple([int(boundary_result[i]), int(boundary_result[i + 1])]))
points = np.array(points)
try:
four_points = minimum_bounding_rectangle(points)
except:
continue
four_points = np.array(four_points).astype(int)
rect = cv2.minAreaRect(four_points)
box = cv2.boxPoints(rect)
oriented_rec = np.int0(box)
x_tl, y_tl = min(oriented_rec[:, 0]), min(oriented_rec[:, 1])
x_br, y_br = max(oriented_rec[:, 0]), max(oriented_rec[:, 1])
if x_tl < 0 or y_tl < 0 or x_br >= np_image.shape[1] or y_br >= np_image.shape[0]:
np_image = cv2.copyMakeBorder(np_image, 500, 500, 500, 500, cv2.BORDER_CONSTANT, value=[0, 0, 0])
np_image = np_image[y_tl + 500 : y_br + 500, x_tl + 500 : x_br + 500]
else:
np_image = np_image[y_tl:y_br, x_tl:x_br]
try:
s = detector.predict(Image.fromarray(np_image))
except:
continue
font_path = os.path.join(cv2.__path__[0], "qt", "fonts", "DejaVuSans.ttf")
font = ImageFont.truetype(font_path, size=16)
draw.text((x_tl, y_tl), str(s), fill="red", font=font)
draw.rectangle([x_tl, y_tl, x_br, y_br], outline="blue")
clockwise = np.flip(oriented_rec, axis=0)
for p in clockwise:
info.append(str(p[0]))
info.append(str(p[1]))
info.append(str(s))
results.append(",".join(info))
# ii.save(folder_path + '/drive/' + file_name + '_txt_.jpg')
file_submit_name = os.path.join(folder_path + "/" + "predicted", file_name + ".txt")
with open(file_submit_name, "w") as file_submit:
for line_string in results:
file_submit.write(line_string)
file_submit.write("\n")
class ModelWrapper:
def __init__(self, args):
self.configCls = args.configCls
self.cls_weights = args.cls_weights
self.rec_weights = args.rec_weights
self.root = args.root
self.input_images = args.input_images
self.output_destination = args.output_destination
self.device = "cuda:0"
# Setting up configuration
def e2e_infer(self, filename, folder_path):
"""
e2e Inferences
filename: image file path
folder_path: prediction archive
Output: Resulting
Postitive:
(VN map) and
do not contain (Truong Sa and Hoang Sa)
Negative:
(not VN map) or
(VN map and contain (Truong Sa or Hoang Sa))
Results:
0: Positive
1: Negative1
2: Negative2
"""
if os.path.exists(folder_path + "/" + "predicted") == False:
os.makedirs(folder_path + "/" + "predicted")
else:
shutil.rmtree(folder_path + "/" + "predicted")
os.makedirs(folder_path + "/" + "predicted")
if os.path.exists(folder_path + "/" + "fig") == False:
os.makedirs(folder_path + "/" + "fig")
else:
shutil.rmtree(folder_path + "/" + "fig")
os.makedirs(folder_path + "/" + "fig")
readtext(folder_path + "/" + "fig", filename)
torch.cuda.empty_cache()
## Recognition
device = self.device
config_rec = Cfg.load_config_from_name("vgg_transformer")
config_rec["weights"] = self.rec_weights
config_rec["cnn"]["pretrained"] = False
config_rec["device"] = device
## Classification
cfg = Config.fromfile(self.configCls)
cfg.device = device
cfg.model.head.num_classes = 2
cfg.model.head.topk = (1,)
cfg.model.backbone.init_cfg = dict(type="Pretrained", checkpoint=self.cls_weights, prefix="backbone")
cfg.model.head.num_classes = 2
cfg.model.head.topk = (1,)
# Model load weights
self.ClsModel = init_model(cfg, self.cls_weights, device=device)
self.ClsModel.CLASSES = ["notvietnam", "vietnam"]
self.ClsModel.cfg = cfg
print("Completed Loading Cls Model")
self.OCRModel = Predictor(config_rec)
print("Completed Loading Rec Model")
results = 0
image_folder_path = folder_path + "/" + "fig"
for f in os.listdir(image_folder_path):
# Read image
try:
image = cv2.imread(filename)
except:
continue
file_type = f.split(".")[-1]
## CLASSIFICATION
data = data_infer(self.ClsModel.cfg, filename, device)
# forward the model
with torch.no_grad():
scores = self.ClsModel(return_loss=False, **data)
pred_score = np.max(scores, axis=1)[0]
pred_label = np.argmax(scores, axis=1)[0]
result = {"pred_label": pred_label, "pred_score": float(pred_score)}
result["pred_class"] = self.ClsModel.CLASSES[result["pred_label"]]
if result["pred_label"] == 0: # Not VietNam
results = 1
continue
ff = None
text_file = os.path.join(image_folder_path, "det_results.txt")
f = open(text_file, "r")
tmp = f.read()
boundary_results = ast.literal_eval(tmp.split("\t")[1].strip())
result_str = []
ii = Image.fromarray(np.array(image))
for boundary_result in boundary_results:
np_image = np.array(image)
info = []
points = []
for i in range(0, len(boundary_result), 1):
points.append(tuple([int(boundary_result[i][0]), int(boundary_result[i][1])]))
points = np.array(points)
try:
four_points = minimum_bounding_rectangle(points)
except:
continue
four_points = np.array(four_points).astype(int)
rect = cv2.minAreaRect(four_points)
box = cv2.boxPoints(rect)
oriented_rec = np.int0(box)
x_tl, y_tl = min(oriented_rec[:, 0]), min(oriented_rec[:, 1])
x_br, y_br = max(oriented_rec[:, 0]), max(oriented_rec[:, 1])
if x_tl < 0 or y_tl < 0 or x_br >= np_image.shape[1] or y_br >= np_image.shape[0]:
np_image = cv2.copyMakeBorder(np_image, 500, 500, 500, 500, cv2.BORDER_CONSTANT, value=[0, 0, 0])
np_image = np_image[y_tl + 500 : y_br + 500, x_tl + 500 : x_br + 500]
else:
np_image = np_image[y_tl:y_br, x_tl:x_br]
try:
s = self.OCRModel.predict(Image.fromarray(np_image))
except:
continue
clockwise = np.flip(oriented_rec, axis=0)
for p in clockwise:
info.append(str(p[0]))
info.append(str(p[1]))
info.append(str(s))
result_str.append(unidecode(info[-1].lower()))
if check(result_str) == 1:
results = 2
else:
results = 0
# Postitive:
# (VN map) and
# do not contain (Truong Sa and Hoang Sa)
# Negative:
# (not VN map) or
# (VN map and contain (Truong Sa or Hoang Sa))
if results == 0:
print("This is VN map. It does not contain (Truong Sa and Hoang Sa).. ALERT")
elif results == 1:
print("This is not VN map.. SKIPPED")
elif results == 2:
print("This is VN map, It contains (Truong Sa or Hoang Sa).. OK")
def process_image(self):
"""
Mass Inference
Outputing logging files -> final results from eval.py
"""
folder_path = self.output_destination
image_folder_path = self.input_images
if os.path.exists(folder_path + "/" + "predicted") == False:
os.makedirs(folder_path + "/" + "predicted")
seg_res = self.SegmentationModel.readtext(
self.input_images, output=self.output_destination, export=self.output_destination
)
self.SegmentationModel = None
torch.cuda.empty_cache()
## Recognition
device = "cuda:0"
config_rec = Cfg.load_config_from_name("vgg_transformer")
config_rec["weights"] = self.rec_weights
config_rec["cnn"]["pretrained"] = False
config_rec["device"] = device
## Classification
cfg = Config.fromfile(self.configCls)
cfg.device = auto_select_device()
cfg.model.head.num_classes = 2
cfg.model.head.topk = (1,)
cfg.model.backbone.init_cfg = dict(type="Pretrained", checkpoint=self.cls_weights, prefix="backbone")
cfg.model.head.num_classes = 2
cfg.model.head.topk = (1,)
# Model load weights
self.ClsModel = init_model(cfg, self.cls_weights, device=device)
self.ClsModel.CLASSES = ["notvietnam", "vietnam"]
self.ClsModel.cfg = cfg
print("Completed Loading Cls Model")
self.OCRModel = Predictor(config_rec)
print("Completed Loading Rec Model")
for f in tqdm(os.listdir(image_folder_path)):
file_type = f.split(".")[-1]
file_name = ".".join(f.split(".")[0:-1])
## CLASSIFICATION
device = "cuda:0"
data = data_infer(self.ClsModel.cfg, os.path.join(image_folder_path, f), device)
# forward the model
with torch.no_grad():
scores = self.ClsModel(return_loss=False, **data)
pred_score = np.max(scores, axis=1)[0]
pred_label = np.argmax(scores, axis=1)[0]
result = {"pred_label": pred_label, "pred_score": float(pred_score)}
result["pred_class"] = self.ClsModel.CLASSES[result["pred_label"]]
if result["pred_label"] == 0: # Not VietNam
file_submit_name = os.path.join(folder_path + "/" + "predicted", file_name + ".txt")
with open(file_submit_name, "w") as file_submit:
file_submit.write("notvnmap")
file_submit.write("\n")
continue
image = cv2.imread(os.path.join(image_folder_path, f))
ff = None
try:
ff = open(os.path.join(folder_path, "out_" + file_name + ".json"), "r")
except:
file_submit_name = os.path.join(folder_path + "/" + "predicted", file_name + ".txt")
with open(file_submit_name, "a") as file_submit:
file_submit.write("vnmap")
file_submit.write("\n")
continue
all_data = json.load(ff)
boundary_results = all_data["boundary_result"]
results = []
ii = Image.fromarray(np.array(image))
# draw = ImageDraw.Draw(ii)
for boundary_result in boundary_results:
np_image = np.array(image)
info = []
if boundary_result[-1] < 0.1:
continue
points = []
for i in range(0, len(boundary_result) - 2, 2):
points.append(tuple([int(boundary_result[i]), int(boundary_result[i + 1])]))
points = np.array(points)
try:
four_points = minimum_bounding_rectangle(points)
except:
continue
four_points = np.array(four_points).astype(int)
rect = cv2.minAreaRect(four_points)
box = cv2.boxPoints(rect)
oriented_rec = np.int0(box)
x_tl, y_tl = min(oriented_rec[:, 0]), min(oriented_rec[:, 1])
x_br, y_br = max(oriented_rec[:, 0]), max(oriented_rec[:, 1])
if x_tl < 0 or y_tl < 0 or x_br >= np_image.shape[1] or y_br >= np_image.shape[0]:
np_image = cv2.copyMakeBorder(np_image, 500, 500, 500, 500, cv2.BORDER_CONSTANT, value=[0, 0, 0])
np_image = np_image[y_tl + 500 : y_br + 500, x_tl + 500 : x_br + 500]
else:
np_image = np_image[y_tl:y_br, x_tl:x_br]
try:
s = self.OCRModel.predict(Image.fromarray(np_image))
except:
continue
# font_path = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
# font = ImageFont.truetype(font_path, size=16)
# draw.text((x_tl, y_tl), str(s), fill="red", font=font)
# draw.rectangle([x_tl, y_tl, x_br, y_br], outline="blue")
clockwise = np.flip(oriented_rec, axis=0)
for p in clockwise:
info.append(str(p[0]))
info.append(str(p[1]))
info.append(str(s))
results.append(",".join(info))
file_submit_name = os.path.join(folder_path + "/" + "predicted", file_name + ".txt")
with open(file_submit_name, "w") as file_submit:
for line_string in results:
file_submit.write(line_string)
file_submit.write("\n")
if __name__ == "__main__":
args = parse_args()
wrapper = ModelWrapper(args)
if args.single_infer == "False":
wrapper.process_image()
else:
wrapper.e2e_infer(args.single_infer_image, args.single_infer_path)