├── requirement.txt ├── main.py ├── scripts ├── reusable_code.py ├── onnx_detector.py └── map_evaluator.py ├── README.md └── LICENSE /requirement.txt: -------------------------------------------------------------------------------- 1 | colorama==0.4.6 2 | coloredlogs==15.0.1 3 | flatbuffers==23.5.26 4 | humanfriendly==10.0 5 | mpmath==1.3.0 6 | numpy==1.21.6 7 | onnxruntime==1.14.1 8 | opencv-python==4.8.0.76 9 | packaging==23.1 10 | Pillow==9.5.0 11 | protobuf==4.24.1 12 | pyreadline==2.1 13 | sympy==1.10.1 14 | tqdm==4.66.1 15 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # main.py 2 | 3 | from scripts.onnx_detector import OnnxDetector 4 | from scripts.map_evaluator import MAPFinder 5 | 6 | if __name__ == "__main__": 7 | model_onnx = "model/name_of_your_model.onnx" 8 | test_dataset = "Test dataset path" 9 | Classes = ["vehicle"] # class name 10 | gt_path = "input/ground-truth" 11 | dt_path = "input/detection-results" 12 | output_json_path = r"input/map.json" 13 | 14 | detector = OnnxDetector(model_onnx, Classes) 15 | detector.mAP_input_data(model_onnx, Classes, test_dataset, gt_path, dt_path) 16 | 17 | evaluator = MAPFinder(gt_path, dt_path, output_json_path) 18 | mAP, ap_dict = evaluator.evaluate_mAP() 19 | 20 | print("mAP:", mAP) 21 | print("Class-wise APs:", ap_dict) 22 | -------------------------------------------------------------------------------- /scripts/reusable_code.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | 3 | 4 | class Reusable_code(): 5 | 6 | def get_image_size(image_path): 7 | try: 8 | img = Image.open(image_path) 9 | width, height = img.size 10 | return width, height 11 | except(IOError, OSError): 12 | return None,None 13 | 14 | def get_image_format(image_path): 15 | try: 16 | for image_file in os.listdir(image_path): 17 | if image_file.endswith('.png'): 18 | img_format = ".png" 19 | return img_format 20 | elif image_file.endswith('.jpg'): 21 | img_format = ".jpg" 22 | return img_format 23 | elif image_file.endswith('.jpeg'): 24 | img_format = ".jpeg" 25 | return img_format 26 | except Exception as e: 27 | print(f"There is some error with image path : {str(e)}") 28 | 29 | 30 | 31 | 32 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Object Detection and mAP Evaluation Toolkit 2 | 3 | This toolkit provides Python scripts for performing object detection using an ONNX model and calculating the mean Average Precision (mAP) for the detection results. 4 | 5 | ## Table of Contents 6 | 7 | - [Overview](#overview) 8 | - [Prerequisites](#prerequisites) 9 | - [Getting Started](#getting-started) 10 | - [Installation](#installation) 11 | - [Usage](#usage) 12 | - [File Structure](#file-structure) 13 | - [License](#license) 14 | 15 | ## Overview 16 | 17 | This toolkit consists of two main parts: 18 | 19 | 1. **Object Detection using ONNX Model (`onnx_detector` module):** 20 | - Utilizes the `OnnxDetector` class to perform object detection using an ONNX model. 21 | - Generates detection results and annotation files. 22 | 23 | 2. **mAP Evaluation (`map_evaluator` module):** 24 | - Utilizes the `MAPFinder` class to calculate the mean Average Precision (mAP) for the detection results. 25 | - Evaluates the detection performance and outputs class-wise APs and mAP. 26 | 27 | ## Prerequisites 28 | 29 | - Python 3.x 30 | - OpenCV (`cv2` module) 31 | - ONNX Runtime (`onnxruntime` package) 32 | - `reusable_code` module (Provided separately) 33 | 34 | ## Getting Started 35 | 36 | ### Installation 37 | 38 | 1. Clone this repository to your local machine: 39 | 40 | git clone https://github.com/Abusheik008/mAP.git 41 | cd mAP 42 | 2. Install the required packages using `pip`: 43 | 44 | pip install -r requirement.txt 45 | 46 | ### Usage 47 | 48 | 1. Prepare your ONNX model, test dataset, ground-truth annotations, and detection results. 49 | 50 | 2. Update the necessary paths and parameters in `main.py`: 51 | - `model_onnx`: Path to your ONNX model file. 52 | - `test_dataset`: Path to the test dataset. 53 | - `Classes`: List of class names. 54 | - `gt_path`: Path to the ground-truth annotations. 55 | - `dt_path`: Path to the detection results. 56 | - `output_json_path`: Path to the output JSON file. 57 | 58 | 3. Run the script: 59 | 60 | python main.py 61 | 62 | 4. Review the output: 63 | - The script will print the calculated mAP and class-wise APs. 64 | - Also it will save the result in JSON file 65 | 66 | ## File Structure 67 | 68 | The file structure is organized as follows: 69 | 70 | ├── main.py # Main execution script 71 | ├── input 72 | │ ├── onnx_detector.py # ONNX detection module 73 | │ └── map_evaluator.py # mAP evaluation module 74 | │ └── reusable_code.py # Reusable code module (provided separately) 75 | ├── input 76 | │ ├── detection-results # Detection result files 77 | │ └── ground-truth # Ground-truth annotation files 78 | ├── model # Directory containing ONNX model 79 | ├── Test dataset # Directory containing test images 80 | ├── requirements.txt # List of required packages 81 | └── README.md # Project documentation 82 | 83 | ## License 84 | 85 | This project is licensed under the GNU GENERAL PUBLIC LICENSE - see the [LICENSE](LICENSE) file for details. 86 | 87 | -------------------------------------------------------------------------------- /scripts/onnx_detector.py: -------------------------------------------------------------------------------- 1 | import onnxruntime 2 | import numpy as np 3 | import os,shutil 4 | import cv2 5 | from tqdm import tqdm 6 | from scripts.reusable_code import Reusable_code 7 | 8 | 9 | class OnnxDetector: 10 | def __init__(self, model_weights, classes): 11 | # Constructor to initialize the OnnxDetector class 12 | # Sets up the ONNX runtime InferenceSession and related attributes 13 | self.net = onnxruntime.InferenceSession(model_weights) 14 | self.classes = classes 15 | self.layer_names = self.net.get_modelmeta() 16 | self.inputlayers = self.net.get_inputs()[0].name 17 | self.outputlayers = [self.net.get_outputs()[0].name, self.net.get_outputs()[1].name] 18 | 19 | @staticmethod 20 | def get_output_format(box): 21 | # Static method to convert bounding box coordinates to a specific format 22 | x, y, w, h = box 23 | return int(x), int(y), int(x+w), int(y+h) 24 | 25 | @staticmethod 26 | def txt_format(height, width, classes, box, key): 27 | # Static method to convert bounding box coordinates to a specific text format 28 | H = height 29 | W = width 30 | x, y, w, h = box 31 | w = str(round(float(w) / W, 5)) 32 | h = str(round(float(h) / H, 5)) 33 | x = str(round((float(x) / W) + (float(w) / 2), 5)) 34 | y = str(round((float(y) / H) + (float(h) / 2), 5)) 35 | annotation_list = ' '.join([str(classes.index(key)), x, y, w, h]) 36 | return annotation_list 37 | 38 | def detect(self, img, conf=0.2, nms_thresh=0.2, non_max_suppression=True, class_conf=None): 39 | # Method for object detection using the ONNX model 40 | threshold = conf 41 | if class_conf is None: 42 | class_conf = [] 43 | if len(class_conf) < len(self.classes): 44 | conf = {k:conf for k in self.classes} 45 | else: 46 | conf = class_conf 47 | class_conf_dict = {k: conf[k] for i, k in enumerate(self.classes)} 48 | final_result = {k: [] for k in self.classes} 49 | final_list = [ ] 50 | fin_same = {k:[] for k in self.classes} 51 | confidences = {k: [] for k in self.classes} 52 | boxes = {k: [] for k in self.classes} 53 | blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), (0, 0, 0), True, crop=False) 54 | layers_result = self.net.run([self.outputlayers[0], self.outputlayers[1]], 55 | {self.inputlayers: blob}) 56 | outputs = np.concatenate([layers_result[1], layers_result[0]], axis=1) 57 | height, width, _ = img.shape 58 | matches = outputs[np.where(np.max(outputs[:, 4:], axis=1) > threshold)] 59 | for detect in matches: 60 | scores = detect[4:] 61 | class_id = np.argmax(scores) 62 | confidence = scores[class_id] 63 | center_x = int(detect[0] * width) 64 | center_y = int(detect[1] * height) 65 | w = int(detect[2] * width) 66 | h = int(detect[3] * height) 67 | x = int(center_x - w/2) 68 | y = int(center_y - h / 2) 69 | confidences[self.classes[class_id]].append(float(confidence)) 70 | boxes[self.classes[class_id]].append([int(i) for i in [x, y, w, h]]) 71 | indices = {} 72 | if non_max_suppression: 73 | for class_name, box in boxes.items(): 74 | indices[class_name] = cv2.dnn.NMSBoxes(box, confidences[class_name], class_conf_dict[class_name], nms_thresh) 75 | else: 76 | for class_name, box in boxes.items(): 77 | indices[class_name] = [[w] for w in range(len(box))] 78 | 79 | for key, index in indices.items(): 80 | for i in index: 81 | try: 82 | select = i[0] 83 | except: 84 | select = i 85 | final_result[key].append(self.get_output_format(boxes[key][select])) 86 | annotation_list = self.txt_format(height, width, self.classes, boxes[key][select], key) 87 | final_list.append(annotation_list) 88 | fin_same[key].append([self.get_output_format(boxes[key][select]), confidences[key][select]]) 89 | return confidences, boxes 90 | 91 | def mAP_input_data(model_onnx,classes,Test_dataset, destination_path_gt, destination_path_result, img_path_dest): 92 | # Method to prepare input data for mean Average Precision (mAP) calculation 93 | os.makedirs(destination_path_gt, exist_ok=True) 94 | os.makedirs(destination_path_result, exist_ok=True) 95 | os.makedirs(img_path_dest, exist_ok=True) 96 | 97 | 98 | if Test_dataset is not None: 99 | for dataset in tqdm(os.listdir(Test_dataset), desc= "Finding and Moving Detection Result"): 100 | if dataset.endswith(".jpg") or dataset.endswith(".png") or dataset.endswith(".jpeg"): 101 | image_path = os.path.join(Test_dataset,dataset) 102 | img_dest = os.path.join(img_path_dest,dataset) 103 | txt_path = os.path.join(Test_dataset,dataset.replace(".jpg", ".txt").replace(".png", ".txt").replace(".jpeg", ".txt")) 104 | detector = OnnxDetector(model_onnx,classes) 105 | print(f"The Image Used Is : {image_path}") 106 | read_img = cv2.imread(image_path) 107 | outputs = detector.detect(read_img) 108 | detections_with_conf, detections = outputs 109 | result_txt_path = os.path.join(destination_path_result,dataset.replace(".jpg", ".txt").replace(".png", ".txt").replace(".jpeg", ".txt")) 110 | with open(result_txt_path, "w") as f: 111 | for class_name, class_detections in detections.items(): 112 | for detection in class_detections: 113 | if detection is not None: 114 | x1, y1, x2, y2 = detection 115 | img_width, img_height= Reusable_code.get_image_size(image_path) 116 | dt_x = (x1 + x2) / 2 / img_width 117 | dt_y = (y1 + y2) / 2 / img_height 118 | dt_width = (x2 - x1) / img_width 119 | dt_height = (y2 - y1) / img_height 120 | confidence = detections_with_conf[class_name][class_detections.index(detection)] 121 | line = f"{class_name} {confidence:.6f} {dt_x:.6f} {dt_y:.6f} {dt_width:.6f} {dt_height:.6f}\n" 122 | f.write(line) 123 | else: 124 | line = f"{class_name} {0} {0} {0} {0} {0}\n" 125 | f.write(line) 126 | # copying txt files 127 | destination_path = os.path.join(destination_path_gt, dataset.replace(".jpg", ".txt").replace(".png", ".txt").replace(".jpeg", ".txt")) 128 | shutil.copy(txt_path, destination_path) 129 | updated_lines = [] 130 | with open(txt_path, "r") as f: 131 | for line in f: 132 | parts = line.strip().split() 133 | if len(parts) >= 5: 134 | parts[0] = "vehicle" 135 | updated_line = " ".join(parts) + "\n" 136 | updated_lines.append(updated_line) 137 | shutil.copy(image_path, img_dest) 138 | 139 | with open(destination_path, "w") as f: 140 | print(f"Updating the line in path :{destination_path} the text : {updated_lines}") 141 | f.writelines(updated_lines) 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | -------------------------------------------------------------------------------- /scripts/map_evaluator.py: -------------------------------------------------------------------------------- 1 | import glob,os 2 | import json 3 | 4 | class MAPFinder: 5 | # Class to calculate mean Average Precision (mAP) for object detection 6 | def __init__(self, annotation_path, results_path, output_json_path): 7 | # Constructor to initialize paths for annotation, detection results, and output JSON 8 | self.annotation_path = annotation_path 9 | self.results_path = results_path 10 | self.output_json_path = output_json_path 11 | 12 | def parse_annotations(self): 13 | # Parse ground truth annotation files and store the information 14 | all_annotations = {} 15 | for txt_file in glob.glob(self.annotation_path + '/*.txt'): 16 | image_name = os.path.basename(txt_file).replace('.txt', '') 17 | image_annotations = [] 18 | 19 | with open(txt_file, 'r') as f: 20 | lines = f.readlines() 21 | 22 | for line in lines: 23 | parts = line.strip().split() 24 | obj_info = { 25 | 'class': parts[0], 26 | 'bbox': [float(parts[1]), float(parts[2]), float(parts[3]), float(parts[4])] 27 | } 28 | image_annotations.append(obj_info) 29 | 30 | all_annotations[image_name] = image_annotations 31 | 32 | return all_annotations 33 | 34 | def parse_detection_results(self): 35 | # Parse detection result files and store the information 36 | detection_results = {} 37 | for txt_file in glob.glob(self.results_path + '/*.txt'): 38 | image_name = os.path.basename(txt_file).replace('.txt', '') 39 | image_detections = [] 40 | 41 | with open(txt_file, 'r') as f: 42 | lines = f.readlines() 43 | 44 | for line in lines: 45 | parts = line.strip().split() 46 | obj_info = { 47 | 'class': parts[0], 48 | 'confidence': float(parts[1]), 49 | 'bbox': [float(parts[1]), float(parts[2]), float(parts[3]), float(parts[4])] 50 | } 51 | image_detections.append(obj_info) 52 | 53 | detection_results[image_name] = image_detections 54 | 55 | return detection_results 56 | 57 | def calculate_iou(self,bbox1, bbox2): 58 | # Calculate Intersection over Union (IoU) between two bounding boxes 59 | x1, y1, w1, h1 = bbox1 60 | x2, y2, w2, h2 = bbox2 61 | 62 | x_overlap = max(0, min(x1 + w1, x2 + w2) - max(x1, x2)) 63 | y_overlap = max(0, min(y1 + h1, y2 + h2) - max(y1, y2)) 64 | 65 | intersection = x_overlap * y_overlap 66 | union = w1 * h1 + w2 * h2 - intersection 67 | 68 | iou = intersection / union 69 | return iou 70 | 71 | def calculate_ap(self,recall, precision): 72 | # Calculate Average Precision (AP) using recall and precision valuesq 73 | recall.insert(0, 0.0) 74 | recall.append(1.0) 75 | precision.insert(0, 0.0) 76 | precision.append(0.0) 77 | 78 | for i in range(len(precision) - 2, -1, -1): 79 | precision[i] = max(precision[i], precision[i + 1]) 80 | 81 | ap = 0.0 82 | for i in range(len(recall) - 1): 83 | ap += (recall[i + 1] - recall[i]) * precision[i + 1] 84 | 85 | return ap 86 | 87 | def evaluate_mAP(self): 88 | # Evaluate mean Average Precision (mAP) and class-wise APs 89 | ground_truth = self.parse_annotations() 90 | detection_results = self.parse_detection_results() 91 | 92 | all_classes = set() 93 | for image_annotations in ground_truth.values(): 94 | for obj_info in image_annotations: 95 | all_classes.add(obj_info['class']) 96 | all_classes = sorted(list(all_classes)) 97 | 98 | ap_dict = {} 99 | for class_name in all_classes: 100 | true_positives = [] 101 | false_positives = [] 102 | 103 | for image_name, image_annotations in ground_truth.items(): 104 | image_detections = detection_results.get(image_name, []) 105 | detected_objects = set() 106 | 107 | for obj_info in image_annotations: 108 | if obj_info['class'] != class_name: 109 | continue 110 | best_iou = 0.0 111 | best_detection = None 112 | 113 | for det_info in image_detections: 114 | if det_info['class'] != class_name: 115 | continue 116 | iou = self.calculate_iou(obj_info['bbox'], det_info['bbox']) 117 | if iou > best_iou: 118 | best_iou = iou 119 | best_detection = det_info 120 | 121 | if best_detection is not None and best_iou >= 0.1: 122 | det_tuple = ( 123 | best_detection['class'], 124 | tuple(best_detection['bbox']) 125 | ) 126 | if det_tuple not in detected_objects: 127 | true_positives.append(1) 128 | detected_objects.add(det_tuple) 129 | else: 130 | false_positives.append(1) 131 | else: 132 | false_positives.append(1) 133 | 134 | num_ground_truth = len(ground_truth) 135 | num_detections = len(detection_results) 136 | num_true_positives = sum(true_positives) 137 | num_false_positives = sum(false_positives) 138 | 139 | 140 | recall = num_true_positives / num_ground_truth if num_ground_truth > 0 else 0 141 | precision = num_true_positives / (num_true_positives + num_false_positives) if (num_true_positives + num_false_positives) > 0 else 0 142 | ap = self.calculate_ap([recall], [precision]) 143 | ap_dict[class_name] = ap 144 | 145 | mAP = sum(ap_dict.values()) / 1 146 | rounded_ap_dict = {class_name: round(ap, 3) for class_name, ap in ap_dict.items()} 147 | 148 | results = { 149 | 'num_ground_truth': num_ground_truth, 150 | 'num_detections': num_detections, 151 | 'num_true_positives': num_true_positives, 152 | 'num_false_positives': num_false_positives, 153 | 'mAP': round(mAP, 3), 154 | 'class_map': rounded_ap_dict, 155 | 'model_new':False 156 | } 157 | 158 | if os.path.exists(self.output_json_path): 159 | with open(self.output_json_path, 'r') as json_file: 160 | current_results = json.load(json_file) 161 | current_mAP = current_results['mAP'] 162 | if round(mAP, 3) > current_mAP: 163 | results_new = { 164 | 'num_ground_truth': num_ground_truth, 165 | 'num_detections': num_detections, 166 | 'num_true_positives': num_true_positives, 167 | 'num_false_positives': num_false_positives, 168 | 'mAP': round(mAP, 3), 169 | 'class_map': rounded_ap_dict, 170 | 'model_new': True 171 | } 172 | with open(self.output_json_path, 'w') as json_file: 173 | json.dump(results_new, json_file, indent=4) 174 | else: 175 | with open(self.output_json_path, 'w') as json_file: 176 | json.dump(results, json_file, indent=4) 177 | 178 | return mAP, ap_dict 179 | 180 | 181 | 182 | 183 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------