| 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 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)
|
| rec.append(1.0)
|
| mrec = rec[:]
|
| prec.insert(0, 0.0)
|
| prec.append(0.0)
|
| 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)
|
| """
|
| 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):
|
|
|
| with open(path) as f:
|
| content = f.readlines()
|
|
|
| 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):
|
|
|
| bb = t.get_window_extent(renderer=r)
|
| text_width_inches = bb.width / fig.dpi
|
|
|
| current_fig_width = fig.get_figwidth()
|
| new_fig_width = current_fig_width + text_width_inches
|
| propotion = new_fig_width / current_fig_width
|
|
|
| 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):
|
|
|
| sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1))
|
|
|
| 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)
|
|
|
| plt.legend(loc='lower right')
|
| """
|
| Write number on side of bar
|
| """
|
| fig = plt.gcf()
|
| axes = plt.gca()
|
| r = fig.canvas.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)
|
|
|
|
|
| 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):
|
| 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()
|
| axes = plt.gca()
|
| r = fig.canvas.get_renderer()
|
| for i, val in enumerate(sorted_values):
|
| str_val = " " + str(val)
|
| if val < 1.0:
|
| str_val = " {0:.2f}".format(val)
|
| t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold')
|
|
|
| if i == (len(sorted_values)-1):
|
| adjust_axes(r, t, fig, axes)
|
|
|
| fig.canvas.set_window_title(window_title)
|
|
|
| tick_font_size = 12
|
| plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size)
|
| """
|
| Re-scale height accordingly
|
| """
|
| init_height = fig.get_figheight()
|
|
|
| dpi = fig.dpi
|
| height_pt = n_classes * (tick_font_size * 1.4)
|
| height_in = height_pt / dpi
|
|
|
| top_margin = 0.15
|
| bottom_margin = 0.05
|
| figure_height = height_in / (1 - top_margin - bottom_margin)
|
|
|
| if figure_height > init_height:
|
| fig.set_figheight(figure_height)
|
|
|
|
|
| plt.title(plot_title, fontsize=14)
|
|
|
|
|
| plt.xlabel(x_label, fontsize='large')
|
|
|
| fig.tight_layout()
|
|
|
| fig.savefig(output_path)
|
|
|
| if to_show:
|
| plt.show()
|
|
|
| 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, left, top, right, bottom, _difficult = line.split()
|
| is_difficult = True
|
| else:
|
| class_name, left, top, right, bottom = line.split()
|
| except:
|
| if "difficult" in line:
|
| line_split = line.split()
|
| _difficult = line_split[-1]
|
| bottom = line_split[-2]
|
| right = line_split[-3]
|
| top = line_split[-4]
|
| left = line_split[-5]
|
| class_name = ""
|
| for name in line_split[:-5]:
|
| class_name += name + " "
|
| class_name = class_name[:-1]
|
| is_difficult = True
|
| else:
|
| line_split = line.split()
|
| bottom = line_split[-1]
|
| right = line_split[-2]
|
| top = line_split[-3]
|
| left = line_split[-4]
|
| class_name = ""
|
| for name in line_split[:-4]:
|
| class_name += name + " "
|
| class_name = class_name[:-1]
|
|
|
| bbox = left + " " + top + " " + right + " " + bottom
|
| 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, left, top, right, bottom = line.split()
|
| except:
|
| line_split = line.split()
|
| bottom = line_split[-1]
|
| right = line_split[-2]
|
| top = line_split[-3]
|
| left = line_split[-4]
|
| confidence = line_split[-5]
|
| tmp_class_name = ""
|
| for name in line_split[:-5]:
|
| tmp_class_name += name + " "
|
| tmp_class_name = tmp_class_name[:-1]
|
|
|
| if tmp_class_name == class_name:
|
| bbox = left + " " + top + " " + right + " " +bottom
|
| 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() ]
|
| bi = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])]
|
| iw = bi[2] - bi[0] + 1
|
| ih = bi[3] - bi[1] + 1
|
| if iw > 0 and ih > 0:
|
| ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
|
| + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
|
| ov = iw * ih / ua
|
| 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
|
|
|
| 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)
|
|
|
| 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 "
|
|
|
| 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.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,
|
| '',
|
| )
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| """
|
| 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 = True
|
| 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
|
|
|
|
|
|
|
|
|
| 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 |
