Welding-Parts-Anomaly-Detection/num_pixel.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2024/6/20 0020 下午 1:44
# @Author  : liudan
# @File    : 11111.py
# @Software: pycharm

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2024/6/20 0020 下午 1:44
# @Author  : liudan
# @File    : 11111.py
# @Software: pycharm

import os
import cv2
import cv2 as cv
import numpy as np
import json
import os


THRESHOLD=0.15

def otsu_binarize(image):

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray_image = cv2.equalizeHist(gray_image)
    # ret1, mask1 = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY +cv2.THRESH_OTSU)
    ret2, mask =cv2.threshold(gray_image,60, 255, cv2.THRESH_BINARY)
    # mask =mask1 & mask2
    kernel = np.ones((3, 3), np.uint8)
    mask = cv2.dilate(mask, kernel, iterations=3)
    mask = cv2.erode(mask, kernel, iterations=3)


    return mask

def read_json_file(json_path):
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    coordinates_dict = {}
    for shape in data['shapes']:
        shape_name = shape['label']
        points = shape['points']
        rounded_points = [[int(round(x)), int(round(y))] for x, y in points]
        coordinates_dict[shape_name] = rounded_points
    return coordinates_dict



def pixel_calculation(ref_image_dir, query_positive_image_dir, query_negative_image_dir, json_path):
    true_positives = 0
    false_positives = 0
    false_negatives = 0
    true_negatives = 0

    false_negatives_list = []
    false_positives_list = []


    for ref_file in os.listdir(ref_image_dir):
        ref_image_path = os.path.join(ref_image_dir, ref_file)
        ref_image = cv2.imread(ref_image_path)
        ostu_ref_image = otsu_binarize(ref_image)


        for query_file in os.listdir(query_positive_image_dir):
            query_image_path = os.path.join(query_positive_image_dir, query_file)
            query_image = cv2.imread(query_image_path)
            ostu_query_image = otsu_binarize(query_image)

            coordinates = read_json_file(json_path)

            num_pixel_list = []
            for _, points in coordinates.items():
                x1, y1 = points[0]
                x2, y2 = points[1]

                region_ref = ostu_ref_image[y1:y2, x1:x2]
                region_query = ostu_query_image[y1:y2, x1:x2]

                num_pixel_region_ref = round((np.sum(region_ref == 0) / (region_ref.shape[0] * region_ref.shape[1])),2)
                num_pixel_region_query = round((np.sum(region_query == 0) / (region_query.shape[0] * region_query.shape[1])),2)

                num_pixel_list.append(num_pixel_region_query)

            flag = any(element <= THRESHOLD for element in num_pixel_list)

            if not flag:
                true_positives += 1
            else:
                false_negatives += 1
                false_negatives_list.append((query_file, ref_file))
                # cv2.imwrite(os.path.join(query_positive_image_dir, f'{query_file}_img.jpg'),ostu_query_image)
                print(f"positive ={num_pixel_list}")



        for query_file in os.listdir(query_negative_image_dir):
            query_image_path = os.path.join(query_negative_image_dir, query_file)
            query_image = cv2.imread(query_image_path)
            ostu_query_image = otsu_binarize(query_image)
            # cv2.imwrite(os.path.join(query_negative_image_dir, '1.jpg'), ostu_query_image)

            coordinates = read_json_file(json_path)

            num_pixel_list = []
            for _, points in coordinates.items():
                x1, y1 = points[0]
                x2, y2 = points[1]

                region_ref = ostu_ref_image[y1:y2, x1:x2]
                region_query = ostu_query_image[y1:y2, x1:x2]

                num_pixel_region_ref = round((np.sum(region_ref == 0) / (region_ref.shape[0] * region_ref.shape[1])), 2)
                num_pixel_region_query = round((np.sum(region_query == 0) / (region_query.shape[0] * region_query.shape[1])), 2)
                num_pixel_list.append(num_pixel_region_query)

            flag = any(element <= THRESHOLD for element in num_pixel_list)

            if flag:
                true_negatives += 1
            else:
                false_positives += 1
                false_positives_list.append((query_file, ref_file))
                cv2.imwrite(os.path.join(query_negative_image_dir, f'{query_file}_img.jpg'), ostu_query_image)
                print(f"negtive ={num_pixel_list}")

    Accurary = (true_positives + true_negatives) / (true_positives + true_negatives + false_positives + false_negatives)
    # Precision = true_positives / (true_positives + false_positives)
    Recall = true_negatives / (true_negatives + false_positives)
    Precision = true_negatives / (true_negatives + false_negatives)
    # Recall = true_positives / (true_positives + false_negatives)

    F1_score = 2 * (Precision * Recall) / (Precision + Recall)

    print(false_negatives_list)
    print(false_positives_list)
    # print(file_name)
    print(f"Accurary:{Accurary: .4f}")
    print(f"Precision: {Precision:.4f}")
    print(f"Recall: {Recall:.4f}")
    print(f"F1 Score: {F1_score:.4f}")


if __name__ == "__main__":
    # ref_image_dir = 'H:\\dataset\\region_samples\\positive_test_img\\R_ref'
    # query_positive_image_dir = 'H:\\dataset\\region_samples\\positive_test_img\\R_pos'
    # query_negative_image_dir = 'H:\\dataset\\region_samples\\positive_test_img\\R_neg'
    # json_path = 'H:\\dataset\\region_samples\\positive_test_img\\R_json\\2.json'
    ref_image_dir = 'H:\\dataset\\A_ref'
    query_positive_image_dir = 'H:\\dataset\\A'
    query_negative_image_dir = 'H:\\dataset\\C'
    json_path = 'H:\\dataset\\B\\image-024_R.json'
    pixel_calculation(ref_image_dir, query_positive_image_dir, query_negative_image_dir, json_path)