import glob import json import math import operator import os import shutil import sys try: from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval except: pass import cv2 import matplotlib matplotlib.use('Agg') from matplotlib import pyplot as plt import numpy as np ''' 0,0 ------> x (width) | | (Left,Top) | *_________ | | | | | y |_________| (height) * (Right,Bottom) ''' def iou_rotate_calculate(boxes1, boxes2): """ 计算旋转面积 boxes1,boxes2格式为x,y,w,h,theta """ area1 = boxes1[2] * boxes1[3] area2 = boxes2[2] * boxes2[3] r1 = ((boxes1[0], boxes1[1]), (boxes1[2], boxes1[3]), boxes1[4]) r2 = ((boxes2[0], boxes2[1]), (boxes2[2], boxes2[3]), boxes2[4]) int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1] if int_pts is not None: order_pts = cv2.convexHull(int_pts, returnPoints=True) int_area = cv2.contourArea(order_pts) ious = int_area * 1.0 / (area1 + area2 - int_area) else: ious = 0 return ious def log_average_miss_rate(precision, fp_cumsum, num_images): """ log-average miss rate: Calculated by averaging miss rates at 9 evenly spaced FPPI points between 10e-2 and 10e0, in log-space. output: lamr | log-average miss rate mr | miss rate fppi | false positives per image references: [1] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of the State of the Art." Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.4 (2012): 743 - 761. """ if precision.size == 0: lamr = 0 mr = 1 fppi = 0 return lamr, mr, fppi fppi = fp_cumsum / float(num_images) mr = (1 - precision) fppi_tmp = np.insert(fppi, 0, -1.0) mr_tmp = np.insert(mr, 0, 1.0) ref = np.logspace(-2.0, 0.0, num = 9) for i, ref_i in enumerate(ref): j = np.where(fppi_tmp <= ref_i)[-1][-1] ref[i] = mr_tmp[j] lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref)))) return lamr, mr, fppi """ throw error and exit """ def error(msg): print(msg) sys.exit(0) """ check if the number is a float between 0.0 and 1.0 """ def is_float_between_0_and_1(value): try: val = float(value) if val > 0.0 and val < 1.0: return True else: return False except ValueError: return False """ Calculate the AP given the recall and precision array 1st) We compute a version of the measured precision/recall curve with precision monotonically decreasing 2nd) We compute the AP as the area under this curve by numerical integration. """ def voc_ap(rec, prec): """ --- Official matlab code VOC2012--- mrec=[0 ; rec ; 1]; mpre=[0 ; prec ; 0]; for i=numel(mpre)-1:-1:1 mpre(i)=max(mpre(i),mpre(i+1)); end i=find(mrec(2:end)~=mrec(1:end-1))+1; ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); """ rec.insert(0, 0.0) # insert 0.0 at begining of list rec.append(1.0) # insert 1.0 at end of list mrec = rec[:] prec.insert(0, 0.0) # insert 0.0 at begining of list prec.append(0.0) # insert 0.0 at end of list mpre = prec[:] """ This part makes the precision monotonically decreasing (goes from the end to the beginning) matlab: for i=numel(mpre)-1:-1:1 mpre(i)=max(mpre(i),mpre(i+1)); """ for i in range(len(mpre)-2, -1, -1): mpre[i] = max(mpre[i], mpre[i+1]) """ This part creates a list of indexes where the recall changes matlab: i=find(mrec(2:end)~=mrec(1:end-1))+1; """ i_list = [] for i in range(1, len(mrec)): if mrec[i] != mrec[i-1]: i_list.append(i) # if it was matlab would be i + 1 """ The Average Precision (AP) is the area under the curve (numerical integration) matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); """ ap = 0.0 for i in i_list: ap += ((mrec[i]-mrec[i-1])*mpre[i]) return ap, mrec, mpre """ Convert the lines of a file to a list """ def file_lines_to_list(path): # open txt file lines to a list with open(path) as f: content = f.readlines() # remove whitespace characters like `\n` at the end of each line content = [x.strip() for x in content] return content """ Draws text in image """ def draw_text_in_image(img, text, pos, color, line_width): font = cv2.FONT_HERSHEY_PLAIN fontScale = 1 lineType = 1 bottomLeftCornerOfText = pos cv2.putText(img, text, bottomLeftCornerOfText, font, fontScale, color, lineType) text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0] return img, (line_width + text_width) """ Plot - adjust axes """ def adjust_axes(r, t, fig, axes): # get text width for re-scaling bb = t.get_window_extent(renderer=r) text_width_inches = bb.width / fig.dpi # get axis width in inches current_fig_width = fig.get_figwidth() new_fig_width = current_fig_width + text_width_inches propotion = new_fig_width / current_fig_width # get axis limit x_lim = axes.get_xlim() axes.set_xlim([x_lim[0], x_lim[1]*propotion]) """ Draw plot using Matplotlib """ def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar): # sort the dictionary by decreasing value, into a list of tuples sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1)) # unpacking the list of tuples into two lists sorted_keys, sorted_values = zip(*sorted_dic_by_value) # if true_p_bar != "": """ Special case to draw in: - green -> TP: True Positives (object detected and matches ground-truth) - red -> FP: False Positives (object detected but does not match ground-truth) - orange -> FN: False Negatives (object not detected but present in the ground-truth) """ fp_sorted = [] tp_sorted = [] for key in sorted_keys: fp_sorted.append(dictionary[key] - true_p_bar[key]) tp_sorted.append(true_p_bar[key]) plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Positive') plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Positive', left=fp_sorted) # add legend plt.legend(loc='lower right') """ Write number on side of bar """ fig = plt.gcf() # gcf - get current figure axes = plt.gca() r = fig.canvas.manager.get_renderer() for i, val in enumerate(sorted_values): fp_val = fp_sorted[i] tp_val = tp_sorted[i] fp_str_val = " " + str(fp_val) tp_str_val = fp_str_val + " " + str(tp_val) # trick to paint multicolor with offset: # first paint everything and then repaint the first number t = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold') plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold') if i == (len(sorted_values)-1): # largest bar adjust_axes(r, t, fig, axes) else: plt.barh(range(n_classes), sorted_values, color=plot_color) """ Write number on side of bar """ fig = plt.gcf() # gcf - get current figure axes = plt.gca() r = fig.canvas.get_renderer() for i, val in enumerate(sorted_values): str_val = " " + str(val) # add a space before if val < 1.0: str_val = " {0:.2f}".format(val) t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold') # re-set axes to show number inside the figure if i == (len(sorted_values)-1): # largest bar adjust_axes(r, t, fig, axes) # set window title fig.canvas.manager.set_window_title(window_title) # write classes in y axis tick_font_size = 12 plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size) """ Re-scale height accordingly """ init_height = fig.get_figheight() # comput the matrix height in points and inches dpi = fig.dpi height_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing) height_in = height_pt / dpi # compute the required figure height top_margin = 0.15 # in percentage of the figure height bottom_margin = 0.05 # in percentage of the figure height figure_height = height_in / (1 - top_margin - bottom_margin) # set new height if figure_height > init_height: fig.set_figheight(figure_height) # set plot title plt.title(plot_title, fontsize=14) # set axis titles # plt.xlabel('classes') plt.xlabel(x_label, fontsize='large') # adjust size of window fig.tight_layout() # save the plot fig.savefig(output_path) # show image if to_show: plt.show() # close the plot plt.close() def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'): GT_PATH = os.path.join(path, 'ground-truth') DR_PATH = os.path.join(path, 'detection-results') IMG_PATH = os.path.join(path, 'images-optional') TEMP_FILES_PATH = os.path.join(path, '.temp_files') RESULTS_FILES_PATH = os.path.join(path, 'results') show_animation = True if os.path.exists(IMG_PATH): for dirpath, dirnames, files in os.walk(IMG_PATH): if not files: show_animation = False else: show_animation = False if not os.path.exists(TEMP_FILES_PATH): os.makedirs(TEMP_FILES_PATH) if os.path.exists(RESULTS_FILES_PATH): shutil.rmtree(RESULTS_FILES_PATH) else: os.makedirs(RESULTS_FILES_PATH) if draw_plot: try: matplotlib.use('TkAgg') except: pass os.makedirs(os.path.join(RESULTS_FILES_PATH, "AP")) os.makedirs(os.path.join(RESULTS_FILES_PATH, "F1")) os.makedirs(os.path.join(RESULTS_FILES_PATH, "Recall")) os.makedirs(os.path.join(RESULTS_FILES_PATH, "Precision")) if show_animation: os.makedirs(os.path.join(RESULTS_FILES_PATH, "images", "detections_one_by_one")) ground_truth_files_list = glob.glob(GT_PATH + '/*.txt') if len(ground_truth_files_list) == 0: error("Error: No ground-truth files found!") ground_truth_files_list.sort() gt_counter_per_class = {} counter_images_per_class = {} for txt_file in ground_truth_files_list: file_id = txt_file.split(".txt", 1)[0] file_id = os.path.basename(os.path.normpath(file_id)) temp_path = os.path.join(DR_PATH, (file_id + ".txt")) if not os.path.exists(temp_path): error_msg = "Error. File not found: {}\n".format(temp_path) error(error_msg) lines_list = file_lines_to_list(txt_file) bounding_boxes = [] is_difficult = False already_seen_classes = [] for line in lines_list: try: if "difficult" in line: class_name, x, y, w, h,angle, _difficult = line.split() is_difficult = True else: class_name, x, y, w, h,angle = line.split() except: if "difficult" in line: line_split = line.split() _difficult = line_split[-1] angle = line_split[-2] h = line_split[-3] w = line_split[-4] y = line_split[-5] x = line_split[-6] class_name = "" for name in line_split[:-6]: class_name += name + " " class_name = class_name[:-1] is_difficult = True else: line_split = line.split() angle = line_split[-1] h = line_split[-2] w = line_split[-3] y = line_split[-4] x = line_split[-5] class_name = "" for name in line_split[:-5]: class_name += name + " " class_name = class_name[:-1] bbox = x + " " + y + " " + w + " " + h + " " + angle if is_difficult: bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True}) is_difficult = False else: bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False}) if class_name in gt_counter_per_class: gt_counter_per_class[class_name] += 1 else: gt_counter_per_class[class_name] = 1 if class_name not in already_seen_classes: if class_name in counter_images_per_class: counter_images_per_class[class_name] += 1 else: counter_images_per_class[class_name] = 1 already_seen_classes.append(class_name) with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile: json.dump(bounding_boxes, outfile) gt_classes = list(gt_counter_per_class.keys()) gt_classes = sorted(gt_classes) n_classes = len(gt_classes) dr_files_list = glob.glob(DR_PATH + '/*.txt') dr_files_list.sort() for class_index, class_name in enumerate(gt_classes): bounding_boxes = [] for txt_file in dr_files_list: file_id = txt_file.split(".txt",1)[0] file_id = os.path.basename(os.path.normpath(file_id)) temp_path = os.path.join(GT_PATH, (file_id + ".txt")) if class_index == 0: if not os.path.exists(temp_path): error_msg = "Error. File not found: {}\n".format(temp_path) error(error_msg) lines = file_lines_to_list(txt_file) for line in lines: try: tmp_class_name, confidence, x, y, w, h,angle = line.split() except: line_split = line.split() angle = line_split[-1] h = line_split[-2] w = line_split[-3] y = line_split[-4] x = line_split[-5] confidence = line_split[-6] tmp_class_name = "" for name in line_split[:-6]: tmp_class_name += name + " " tmp_class_name = tmp_class_name[:-1] if tmp_class_name == class_name: bbox = x + " " + y + " " + w + " " + h + " " + angle bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox}) bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True) with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile: json.dump(bounding_boxes, outfile) sum_AP = 0.0 ap_dictionary = {} lamr_dictionary = {} with open(RESULTS_FILES_PATH + "/results.txt", 'w') as results_file: results_file.write("# AP and precision/recall per class\n") count_true_positives = {} for class_index, class_name in enumerate(gt_classes): count_true_positives[class_name] = 0 dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json" dr_data = json.load(open(dr_file)) nd = len(dr_data) tp = [0] * nd fp = [0] * nd score = [0] * nd score_threhold_idx = 0 for idx, detection in enumerate(dr_data): file_id = detection["file_id"] score[idx] = float(detection["confidence"]) if score[idx] >= score_threhold: score_threhold_idx = idx if show_animation: ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*") if len(ground_truth_img) == 0: error("Error. Image not found with id: " + file_id) elif len(ground_truth_img) > 1: error("Error. Multiple image with id: " + file_id) else: img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0]) img_cumulative_path = RESULTS_FILES_PATH + "/images/" + ground_truth_img[0] if os.path.isfile(img_cumulative_path): img_cumulative = cv2.imread(img_cumulative_path) else: img_cumulative = img.copy() bottom_border = 60 BLACK = [0, 0, 0] img = cv2.copyMakeBorder(img, 0, bottom_border, 0, 0, cv2.BORDER_CONSTANT, value=BLACK) gt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json" ground_truth_data = json.load(open(gt_file)) ovmax = -1 gt_match = -1 bb = [float(x) for x in detection["bbox"].split()] for obj in ground_truth_data: if obj["class_name"] == class_name: bbgt = [float(x) for x in obj["bbox"].split() ] box1 = np.array([bb[0], bb[1], bb[2], bb[3], bb[4]], np.float32) box2 = np.array([bbgt[0], bbgt[1], bbgt[2], bbgt[3], bbgt[4]], np.float32) ov = iou_rotate_calculate(box1, box2) if ov > ovmax: ovmax = ov gt_match = obj if show_animation: status = "NO MATCH FOUND!" min_overlap = MINOVERLAP if ovmax >= min_overlap: if "difficult" not in gt_match: if not bool(gt_match["used"]): tp[idx] = 1 gt_match["used"] = True count_true_positives[class_name] += 1 with open(gt_file, 'w') as f: f.write(json.dumps(ground_truth_data)) if show_animation: status = "MATCH!" else: fp[idx] = 1 if show_animation: status = "REPEATED MATCH!" else: fp[idx] = 1 if ovmax > 0: status = "INSUFFICIENT OVERLAP" """ Draw image to show animation """ if show_animation: height, widht = img.shape[:2] white = (255,255,255) light_blue = (255,200,100) green = (0,255,0) light_red = (30,30,255) margin = 10 # 1nd line v_pos = int(height - margin - (bottom_border / 2.0)) text = "Image: " + ground_truth_img[0] + " " img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0) text = "Class [" + str(class_index) + "/" + str(n_classes) + "]: " + class_name + " " img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), light_blue, line_width) if ovmax != -1: color = light_red if status == "INSUFFICIENT OVERLAP": text = "IoU: {0:.2f}% ".format(ovmax*100) + "< {0:.2f}% ".format(min_overlap*100) else: text = "IoU: {0:.2f}% ".format(ovmax*100) + ">= {0:.2f}% ".format(min_overlap*100) color = green img, _ = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width) # 2nd line v_pos += int(bottom_border / 2.0) rank_pos = str(idx+1) text = "Detection #rank: " + rank_pos + " confidence: {0:.2f}% ".format(float(detection["confidence"])*100) img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0) color = light_red if status == "MATCH!": color = green text = "Result: " + status + " " img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width) font = cv2.FONT_HERSHEY_SIMPLEX if ovmax > 0: bbgt = [ int(round(float(x))) for x in gt_match["bbox"].split() ] cv2.rectangle(img,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2) cv2.rectangle(img_cumulative,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2) cv2.putText(img_cumulative, class_name, (bbgt[0],bbgt[1] - 5), font, 0.6, light_blue, 1, cv2.LINE_AA) bb = [int(i) for i in bb] cv2.rectangle(img,(bb[0],bb[1]),(bb[2],bb[3]),color,2) cv2.rectangle(img_cumulative,(bb[0],bb[1]),(bb[2],bb[3]),color,2) cv2.putText(img_cumulative, class_name, (bb[0],bb[1] - 5), font, 0.6, color, 1, cv2.LINE_AA) cv2.imshow("Animation", img) cv2.waitKey(20) output_img_path = RESULTS_FILES_PATH + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg" cv2.imwrite(output_img_path, img) cv2.imwrite(img_cumulative_path, img_cumulative) cumsum = 0 for idx, val in enumerate(fp): fp[idx] += cumsum cumsum += val cumsum = 0 for idx, val in enumerate(tp): tp[idx] += cumsum cumsum += val rec = tp[:] for idx, val in enumerate(tp): rec[idx] = float(tp[idx]) / np.maximum(gt_counter_per_class[class_name], 1) prec = tp[:] for idx, val in enumerate(tp): prec[idx] = float(tp[idx]) / np.maximum((fp[idx] + tp[idx]), 1) ap, mrec, mprec = voc_ap(rec[:], prec[:]) F1 = np.array(rec)*np.array(prec)*2 / np.where((np.array(prec)+np.array(rec))==0, 1, (np.array(prec)+np.array(rec))) sum_AP += ap text = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100) if len(prec)>0: F1_text = "{0:.2f}".format(F1[score_threhold_idx]) + " = " + class_name + " F1 " Recall_text = "{0:.2f}%".format(rec[score_threhold_idx]*100) + " = " + class_name + " Recall " Precision_text = "{0:.2f}%".format(prec[score_threhold_idx]*100) + " = " + class_name + " Precision " else: F1_text = "0.00" + " = " + class_name + " F1 " Recall_text = "0.00%" + " = " + class_name + " Recall " Precision_text = "0.00%" + " = " + class_name + " Precision " rounded_prec = [ '%.2f' % elem for elem in prec ] rounded_rec = [ '%.2f' % elem for elem in rec ] results_file.write(text + "\n Precision: " + str(rounded_prec) + "\n Recall :" + str(rounded_rec) + "\n\n") if len(prec)>0: print(text + "\t||\tscore_threhold=" + str(score_threhold) + " : " + "F1=" + "{0:.2f}".format(F1[score_threhold_idx])\ + " ; Recall=" + "{0:.2f}%".format(rec[score_threhold_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score_threhold_idx]*100)) else: print(text + "\t||\tscore_threhold=" + str(score_threhold) + " : " + "F1=0.00% ; Recall=0.00% ; Precision=0.00%") ap_dictionary[class_name] = ap n_images = counter_images_per_class[class_name] lamr, mr, fppi = log_average_miss_rate(np.array(rec), np.array(fp), n_images) lamr_dictionary[class_name] = lamr if draw_plot: plt.plot(rec, prec, '-o') area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]] area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]] plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r') fig = plt.gcf() fig.canvas.manager.set_window_title('AP ' + class_name) plt.title('class: ' + text) plt.xlabel('Recall') plt.ylabel('Precision') axes = plt.gca() axes.set_xlim([0.0,1.0]) axes.set_ylim([0.0,1.05]) fig.savefig(RESULTS_FILES_PATH + "/AP/" + class_name + ".png") plt.cla() plt.plot(score, F1, "-", color='orangered') plt.title('class: ' + F1_text + "\nscore_threhold=" + str(score_threhold)) plt.xlabel('Score_Threhold') plt.ylabel('F1') axes = plt.gca() axes.set_xlim([0.0,1.0]) axes.set_ylim([0.0,1.05]) fig.savefig(RESULTS_FILES_PATH + "/F1/" + class_name + ".png") plt.cla() plt.plot(score, rec, "-H", color='gold') plt.title('class: ' + Recall_text + "\nscore_threhold=" + str(score_threhold)) plt.xlabel('Score_Threhold') plt.ylabel('Recall') axes = plt.gca() axes.set_xlim([0.0,1.0]) axes.set_ylim([0.0,1.05]) fig.savefig(RESULTS_FILES_PATH + "/Recall/" + class_name + ".png") plt.cla() plt.plot(score, prec, "-s", color='palevioletred') plt.title('class: ' + Precision_text + "\nscore_threhold=" + str(score_threhold)) plt.xlabel('Score_Threhold') plt.ylabel('Precision') axes = plt.gca() axes.set_xlim([0.0,1.0]) axes.set_ylim([0.0,1.05]) fig.savefig(RESULTS_FILES_PATH + "/Precision/" + class_name + ".png") plt.cla() if show_animation: cv2.destroyAllWindows() if n_classes == 0: print("未检测到任何种类，请检查标签信息与get_map.py中的classes_path是否修改。") return 0 results_file.write("\n# mAP of all classes\n") mAP = sum_AP / n_classes text = "mAP = {0:.2f}%".format(mAP*100) results_file.write(text + "\n") print(text) shutil.rmtree(TEMP_FILES_PATH) """ Count total of detection-results """ det_counter_per_class = {} for txt_file in dr_files_list: lines_list = file_lines_to_list(txt_file) for line in lines_list: class_name = line.split()[0] if class_name in det_counter_per_class: det_counter_per_class[class_name] += 1 else: det_counter_per_class[class_name] = 1 dr_classes = list(det_counter_per_class.keys()) """ Write number of ground-truth objects per class to results.txt """ with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file: results_file.write("\n# Number of ground-truth objects per class\n") for class_name in sorted(gt_counter_per_class): results_file.write(class_name + ": " + str(gt_counter_per_class[class_name]) + "\n") """ Finish counting true positives """ for class_name in dr_classes: if class_name not in gt_classes: count_true_positives[class_name] = 0 """ Write number of detected objects per class to results.txt """ with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file: results_file.write("\n# Number of detected objects per class\n") for class_name in sorted(dr_classes): n_det = det_counter_per_class[class_name] text = class_name + ": " + str(n_det) text += " (tp:" + str(count_true_positives[class_name]) + "" text += ", fp:" + str(n_det - count_true_positives[class_name]) + ")\n" results_file.write(text) """ Plot the total number of occurences of each class in the ground-truth """ if draw_plot: window_title = "ground-truth-info" plot_title = "ground-truth\n" plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)" x_label = "Number of objects per class" output_path = RESULTS_FILES_PATH + "/ground-truth-info.png" to_show = False plot_color = 'forestgreen' draw_plot_func( gt_counter_per_class, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, '', ) # """ # Plot the total number of occurences of each class in the "detection-results" folder # """ # if draw_plot: # window_title = "detection-results-info" # # Plot title # plot_title = "detection-results\n" # plot_title += "(" + str(len(dr_files_list)) + " files and " # count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values())) # plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)" # # end Plot title # x_label = "Number of objects per class" # output_path = RESULTS_FILES_PATH + "/detection-results-info.png" # to_show = False # plot_color = 'forestgreen' # true_p_bar = count_true_positives # draw_plot_func( # det_counter_per_class, # len(det_counter_per_class), # window_title, # plot_title, # x_label, # output_path, # to_show, # plot_color, # true_p_bar # ) """ Draw log-average miss rate plot (Show lamr of all classes in decreasing order) """ if draw_plot: window_title = "lamr" plot_title = "log-average miss rate" x_label = "log-average miss rate" output_path = RESULTS_FILES_PATH + "/lamr.png" to_show = False plot_color = 'royalblue' draw_plot_func( lamr_dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, "" ) """ Draw mAP plot (Show AP's of all classes in decreasing order) """ if draw_plot: window_title = "mAP" plot_title = "mAP = {0:.2f}%".format(mAP*100) x_label = "Average Precision" output_path = RESULTS_FILES_PATH + "/mAP.png" to_show = False plot_color = 'royalblue' draw_plot_func( ap_dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, "" ) return mAP def preprocess_gt(gt_path, class_names): image_ids = os.listdir(gt_path) results = {} images = [] bboxes = [] for i, image_id in enumerate(image_ids): lines_list = file_lines_to_list(os.path.join(gt_path, image_id)) boxes_per_image = [] image = {} image_id = os.path.splitext(image_id)[0] image['file_name'] = image_id + '.jpg' image['width'] = 1 image['height'] = 1 #-----------------------------------------------------------------# # 感谢 多学学英语吧 的提醒 # 解决了'Results do not correspond to current coco set'问题 #-----------------------------------------------------------------# image['id'] = str(image_id) for line in lines_list: difficult = 0 if "difficult" in line: line_split = line.split() left, top, right, bottom, _difficult = line_split[-5:] class_name = "" for name in line_split[:-5]: class_name += name + " " class_name = class_name[:-1] difficult = 1 else: line_split = line.split() left, top, right, bottom = line_split[-4:] class_name = "" for name in line_split[:-4]: class_name += name + " " class_name = class_name[:-1] left, top, right, bottom = float(left), float(top), float(right), float(bottom) if class_name not in class_names: continue cls_id = class_names.index(class_name) + 1 bbox = [left, top, right - left, bottom - top, difficult, str(image_id), cls_id, (right - left) * (bottom - top) - 10.0] boxes_per_image.append(bbox) images.append(image) bboxes.extend(boxes_per_image) results['images'] = images categories = [] for i, cls in enumerate(class_names): category = {} category['supercategory'] = cls category['name'] = cls category['id'] = i + 1 categories.append(category) results['categories'] = categories annotations = [] for i, box in enumerate(bboxes): annotation = {} annotation['area'] = box[-1] annotation['category_id'] = box[-2] annotation['image_id'] = box[-3] annotation['iscrowd'] = box[-4] annotation['bbox'] = box[:4] annotation['id'] = i annotations.append(annotation) results['annotations'] = annotations return results def preprocess_dr(dr_path, class_names): image_ids = os.listdir(dr_path) results = [] for image_id in image_ids: lines_list = file_lines_to_list(os.path.join(dr_path, image_id)) image_id = os.path.splitext(image_id)[0] for line in lines_list: line_split = line.split() confidence, left, top, right, bottom = line_split[-5:] class_name = "" for name in line_split[:-5]: class_name += name + " " class_name = class_name[:-1] left, top, right, bottom = float(left), float(top), float(right), float(bottom) result = {} result["image_id"] = str(image_id) if class_name not in class_names: continue result["category_id"] = class_names.index(class_name) + 1 result["bbox"] = [left, top, right - left, bottom - top] result["score"] = float(confidence) results.append(result) return results def get_coco_map(class_names, path): GT_PATH = os.path.join(path, 'ground-truth') DR_PATH = os.path.join(path, 'detection-results') COCO_PATH = os.path.join(path, 'coco_eval') if not os.path.exists(COCO_PATH): os.makedirs(COCO_PATH) GT_JSON_PATH = os.path.join(COCO_PATH, 'instances_gt.json') DR_JSON_PATH = os.path.join(COCO_PATH, 'instances_dr.json') with open(GT_JSON_PATH, "w") as f: results_gt = preprocess_gt(GT_PATH, class_names) json.dump(results_gt, f, indent=4) with open(DR_JSON_PATH, "w") as f: results_dr = preprocess_dr(DR_PATH, class_names) json.dump(results_dr, f, indent=4) if len(results_dr) == 0: print("未检测到任何目标。") return [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] cocoGt = COCO(GT_JSON_PATH) cocoDt = cocoGt.loadRes(DR_JSON_PATH) cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() return cocoEval.stats