forked from TensorLayer/tensorlayer3
888 lines
33 KiB
Plaintext
888 lines
33 KiB
Plaintext
import glob
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import json
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import os
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import shutil
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import operator
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import sys
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import argparse
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import math
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import numpy as np
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#用于计算mAP
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MINOVERLAP = 0.5
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parser = argparse.ArgumentParser()
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parser.add_argument('-na', '--no-animation', help="no animation is shown.", action="store_true")
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parser.add_argument('-np', '--no-plot', help="no plot is shown.", action="store_true")
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parser.add_argument('-q', '--quiet', help="minimalistic console output.", action="store_true")
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parser.add_argument('-i', '--ignore', nargs='+', type=str, help="ignore a list of classes.")
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parser.add_argument('--set-class-iou', nargs='+', type=str, help="set IoU for a specific class.")
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args = parser.parse_args()
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if args.ignore is None:
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args.ignore = []
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specific_iou_flagged = False
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if args.set_class_iou is not None:
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specific_iou_flagged = True
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os.chdir(os.path.dirname(os.path.abspath(__file__)))
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GT_PATH = os.path.join(os.getcwd(), 'input', 'ground-truth')
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DR_PATH = os.path.join(os.getcwd(), 'input', 'detection-results')
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IMG_PATH = os.path.join(os.getcwd(), 'input', 'images-optional')
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if os.path.exists(IMG_PATH):
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for dirpath, dirnames, files in os.walk(IMG_PATH):
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if not files:
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args.no_animation = True
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else:
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args.no_animation = True
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show_animation = False
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if not args.no_animation:
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try:
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import cv2
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show_animation = True
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except ImportError:
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print("\"opencv-python\" not found, please install to visualize the results.")
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args.no_animation = True
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draw_plot = False
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if not args.no_plot:
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try:
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import matplotlib.pyplot as plt
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draw_plot = True
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except ImportError:
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print("\"matplotlib\" not found, please install it to get the resulting plots.")
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args.no_plot = True
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def log_average_miss_rate(precision, fp_cumsum, num_images):
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"""
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log-average miss rate:
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Calculated by averaging miss rates at 9 evenly spaced FPPI points
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between 10e-2 and 10e0, in log-space.
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output:
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lamr | log-average miss rate
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mr | miss rate
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fppi | false positives per image
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references:
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[1] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of the
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State of the Art." Pattern Analysis and Machine Intelligence, IEEE
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Transactions on 34.4 (2012): 743 - 761.
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"""
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if precision.size == 0:
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lamr = 0
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mr = 1
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fppi = 0
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return lamr, mr, fppi
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fppi = fp_cumsum / float(num_images)
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mr = (1 - precision)
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fppi_tmp = np.insert(fppi, 0, -1.0)
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mr_tmp = np.insert(mr, 0, 1.0)
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ref = np.logspace(-2.0, 0.0, num = 9)
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for i, ref_i in enumerate(ref):
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j = np.where(fppi_tmp <= ref_i)[-1][-1]
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ref[i] = mr_tmp[j]
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lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref))))
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return lamr, mr, fppi
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"""
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throw error and exit
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"""
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def error(msg):
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print(msg)
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sys.exit(0)
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"""
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check if the number is a float between 0.0 and 1.0
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"""
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def is_float_between_0_and_1(value):
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try:
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val = float(value)
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if val > 0.0 and val < 1.0:
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return True
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else:
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return False
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except ValueError:
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return False
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"""
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Calculate the AP given the recall and precision array
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1st) We compute a version of the measured precision/recall curve with
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precision monotonically decreasing
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2nd) We compute the AP as the area under this curve by numerical integration.
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"""
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def voc_ap(rec, prec):
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"""
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--- Official matlab code VOC2012---
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mrec=[0 ; rec ; 1];
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mpre=[0 ; prec ; 0];
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for i=numel(mpre)-1:-1:1
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mpre(i)=max(mpre(i),mpre(i+1));
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end
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i=find(mrec(2:end)~=mrec(1:end-1))+1;
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ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
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"""
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rec.insert(0, 0.0) # insert 0.0 at begining of list
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rec.append(1.0) # insert 1.0 at end of list
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mrec = rec[:]
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prec.insert(0, 0.0) # insert 0.0 at begining of list
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prec.append(0.0) # insert 0.0 at end of list
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mpre = prec[:]
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"""
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This part makes the precision monotonically decreasing
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(goes from the end to the beginning)
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matlab: for i=numel(mpre)-1:-1:1
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mpre(i)=max(mpre(i),mpre(i+1));
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"""
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for i in range(len(mpre)-2, -1, -1):
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mpre[i] = max(mpre[i], mpre[i+1])
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"""
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This part creates a list of indexes where the recall changes
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matlab: i=find(mrec(2:end)~=mrec(1:end-1))+1;
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"""
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i_list = []
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for i in range(1, len(mrec)):
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if mrec[i] != mrec[i-1]:
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i_list.append(i) # if it was matlab would be i + 1
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"""
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The Average Precision (AP) is the area under the curve
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(numerical integration)
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matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
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"""
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ap = 0.0
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for i in i_list:
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ap += ((mrec[i]-mrec[i-1])*mpre[i])
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return ap, mrec, mpre
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"""
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Convert the lines of a file to a list
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"""
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def file_lines_to_list(path):
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# open txt file lines to a list
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with open(path) as f:
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content = f.readlines()
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# remove whitespace characters like `\n` at the end of each line
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content = [x.strip() for x in content]
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return content
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"""
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Draws text in image
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"""
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def draw_text_in_image(img, text, pos, color, line_width):
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font = cv2.FONT_HERSHEY_PLAIN
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fontScale = 1
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lineType = 1
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bottomLeftCornerOfText = pos
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cv2.putText(img, text,
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bottomLeftCornerOfText,
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font,
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fontScale,
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color,
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lineType)
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text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0]
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return img, (line_width + text_width)
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"""
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Plot - adjust axes
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"""
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def adjust_axes(r, t, fig, axes):
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# get text width for re-scaling
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bb = t.get_window_extent(renderer=r)
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text_width_inches = bb.width / fig.dpi
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# get axis width in inches
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current_fig_width = fig.get_figwidth()
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new_fig_width = current_fig_width + text_width_inches
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propotion = new_fig_width / current_fig_width
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# get axis limit
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x_lim = axes.get_xlim()
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axes.set_xlim([x_lim[0], x_lim[1]*propotion])
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"""
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Draw plot using Matplotlib
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"""
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def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar):
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# sort the dictionary by decreasing value, into a list of tuples
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sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1))
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# unpacking the list of tuples into two lists
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sorted_keys, sorted_values = zip(*sorted_dic_by_value)
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#
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if true_p_bar != "":
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"""
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Special case to draw in:
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- green -> TP: True Positives (object detected and matches ground-truth)
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- red -> FP: False Positives (object detected but does not match ground-truth)
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- orange -> FN: False Negatives (object not detected but present in the ground-truth)
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"""
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fp_sorted = []
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tp_sorted = []
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for key in sorted_keys:
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fp_sorted.append(dictionary[key] - true_p_bar[key])
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tp_sorted.append(true_p_bar[key])
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plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Positive')
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plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Positive', left=fp_sorted)
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# add legend
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plt.legend(loc='lower right')
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"""
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Write number on side of bar
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"""
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fig = plt.gcf() # gcf - get current figure
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axes = plt.gca()
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r = fig.canvas.get_renderer()
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for i, val in enumerate(sorted_values):
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fp_val = fp_sorted[i]
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tp_val = tp_sorted[i]
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fp_str_val = " " + str(fp_val)
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tp_str_val = fp_str_val + " " + str(tp_val)
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# trick to paint multicolor with offset:
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# first paint everything and then repaint the first number
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t = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold')
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plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold')
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if i == (len(sorted_values)-1): # largest bar
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adjust_axes(r, t, fig, axes)
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else:
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plt.barh(range(n_classes), sorted_values, color=plot_color)
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"""
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Write number on side of bar
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"""
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fig = plt.gcf() # gcf - get current figure
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axes = plt.gca()
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r = fig.canvas.get_renderer()
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for i, val in enumerate(sorted_values):
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str_val = " " + str(val) # add a space before
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if val < 1.0:
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str_val = " {0:.2f}".format(val)
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t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold')
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# re-set axes to show number inside the figure
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if i == (len(sorted_values)-1): # largest bar
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adjust_axes(r, t, fig, axes)
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# set window title
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fig.canvas.set_window_title(window_title)
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# write classes in y axis
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tick_font_size = 12
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plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size)
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"""
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Re-scale height accordingly
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"""
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init_height = fig.get_figheight()
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# comput the matrix height in points and inches
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dpi = fig.dpi
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height_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing)
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height_in = height_pt / dpi
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# compute the required figure height
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top_margin = 0.15 # in percentage of the figure height
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bottom_margin = 0.05 # in percentage of the figure height
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figure_height = height_in / (1 - top_margin - bottom_margin)
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# set new height
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if figure_height > init_height:
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fig.set_figheight(figure_height)
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# set plot title
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plt.title(plot_title, fontsize=14)
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# set axis titles
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# plt.xlabel('classes')
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plt.xlabel(x_label, fontsize='large')
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# adjust size of window
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fig.tight_layout()
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# save the plot
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fig.savefig(output_path)
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# show image
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if to_show:
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plt.show()
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# close the plot
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plt.close()
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"""
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Create a ".temp_files/" and "results/" directory
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"""
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TEMP_FILES_PATH = ".temp_files"
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if not os.path.exists(TEMP_FILES_PATH): # if it doesn't exist already
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os.makedirs(TEMP_FILES_PATH)
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results_files_path = "results"
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if os.path.exists(results_files_path): # if it exist already
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# reset the results directory
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shutil.rmtree(results_files_path)
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os.makedirs(results_files_path)
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if draw_plot:
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os.makedirs(os.path.join(results_files_path, "AP"))
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os.makedirs(os.path.join(results_files_path, "F1"))
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os.makedirs(os.path.join(results_files_path, "Recall"))
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os.makedirs(os.path.join(results_files_path, "Precision"))
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if show_animation:
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os.makedirs(os.path.join(results_files_path, "images", "detections_one_by_one"))
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"""
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ground-truth
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Load each of the ground-truth files into a temporary ".json" file.
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Create a list of all the class names present in the ground-truth (gt_classes).
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"""
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# get a list with the ground-truth files
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ground_truth_files_list = glob.glob(GT_PATH + '/*.txt')
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if len(ground_truth_files_list) == 0:
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error("Error: No ground-truth files found!")
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ground_truth_files_list.sort()
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# dictionary with counter per class
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gt_counter_per_class = {}
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counter_images_per_class = {}
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for txt_file in ground_truth_files_list:
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#print(txt_file)
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file_id = txt_file.split(".txt", 1)[0]
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file_id = os.path.basename(os.path.normpath(file_id))
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# check if there is a correspondent detection-results file
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temp_path = os.path.join(DR_PATH, (file_id + ".txt"))
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if not os.path.exists(temp_path):
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error_msg = "Error. File not found: {}\n".format(temp_path)
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error_msg += "(You can avoid this error message by running extra/intersect-gt-and-dr.py)"
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error(error_msg)
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lines_list = file_lines_to_list(txt_file)
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# create ground-truth dictionary
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bounding_boxes = []
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is_difficult = False
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already_seen_classes = []
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for line in lines_list:
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try:
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if "difficult" in line:
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class_name, left, top, right, bottom, _difficult = line.split()
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is_difficult = True
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else:
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class_name, left, top, right, bottom = line.split()
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except:
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if "difficult" in line:
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line_split = line.split()
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_difficult = line_split[-1]
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bottom = line_split[-2]
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right = line_split[-3]
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top = line_split[-4]
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left = line_split[-5]
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class_name = ""
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for name in line_split[:-5]:
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class_name += name + " "
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class_name = class_name[:-1]
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is_difficult = True
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else:
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line_split = line.split()
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bottom = line_split[-1]
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right = line_split[-2]
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top = line_split[-3]
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left = line_split[-4]
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class_name = ""
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for name in line_split[:-4]:
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class_name += name + " "
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class_name = class_name[:-1]
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if class_name in args.ignore:
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continue
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bbox = left + " " + top + " " + right + " " +bottom
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if is_difficult:
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bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True})
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is_difficult = False
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else:
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bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False})
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if class_name in gt_counter_per_class:
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gt_counter_per_class[class_name] += 1
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else:
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gt_counter_per_class[class_name] = 1
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if class_name not in already_seen_classes:
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if class_name in counter_images_per_class:
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counter_images_per_class[class_name] += 1
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else:
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counter_images_per_class[class_name] = 1
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already_seen_classes.append(class_name)
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with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile:
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json.dump(bounding_boxes, outfile)
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gt_classes = list(gt_counter_per_class.keys())
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gt_classes = sorted(gt_classes)
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n_classes = len(gt_classes)
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"""
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Check format of the flag --set-class-iou (if used)
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e.g. check if class exists
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"""
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if specific_iou_flagged:
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n_args = len(args.set_class_iou)
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error_msg = \
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'\n --set-class-iou [class_1] [IoU_1] [class_2] [IoU_2] [...]'
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if n_args % 2 != 0:
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error('Error, missing arguments. Flag usage:' + error_msg)
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# [class_1] [IoU_1] [class_2] [IoU_2]
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# specific_iou_classes = ['class_1', 'class_2']
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specific_iou_classes = args.set_class_iou[::2] # even
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# iou_list = ['IoU_1', 'IoU_2']
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iou_list = args.set_class_iou[1::2] # odd
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if len(specific_iou_classes) != len(iou_list):
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error('Error, missing arguments. Flag usage:' + error_msg)
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for tmp_class in specific_iou_classes:
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if tmp_class not in gt_classes:
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error('Error, unknown class \"' + tmp_class + '\". Flag usage:' + error_msg)
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for num in iou_list:
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if not is_float_between_0_and_1(num):
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error('Error, IoU must be between 0.0 and 1.0. Flag usage:' + error_msg)
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"""
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detection-results
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Load each of the detection-results files into a temporary ".json" file.
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"""
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dr_files_list = glob.glob(DR_PATH + '/*.txt')
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dr_files_list.sort()
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for class_index, class_name in enumerate(gt_classes):
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bounding_boxes = []
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for txt_file in dr_files_list:
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file_id = txt_file.split(".txt",1)[0]
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file_id = os.path.basename(os.path.normpath(file_id))
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temp_path = os.path.join(GT_PATH, (file_id + ".txt"))
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if class_index == 0:
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if not os.path.exists(temp_path):
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error_msg = "Error. File not found: {}\n".format(temp_path)
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error_msg += "(You can avoid this error message by running extra/intersect-gt-and-dr.py)"
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error(error_msg)
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lines = file_lines_to_list(txt_file)
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for line in lines:
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try:
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tmp_class_name, confidence, left, top, right, bottom = line.split()
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except:
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line_split = line.split()
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bottom = line_split[-1]
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right = line_split[-2]
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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)
|
|
|
|
"""
|
|
Calculate the AP for each class
|
|
"""
|
|
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
|
|
"""
|
|
Load detection-results of that class
|
|
"""
|
|
dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json"
|
|
dr_data = json.load(open(dr_file))
|
|
"""
|
|
Assign detection-results to ground-truth objects
|
|
"""
|
|
nd = len(dr_data)
|
|
tp = [0] * nd
|
|
fp = [0] * nd
|
|
score = [0] * nd
|
|
score05_idx = 0
|
|
for idx, detection in enumerate(dr_data):
|
|
file_id = detection["file_id"]
|
|
score[idx] = float(detection["confidence"])
|
|
if score[idx] > 0.5:
|
|
score05_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:
|
|
# compute overlap (IoU) = area of intersection / area of union
|
|
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 specific_iou_flagged:
|
|
if class_name in specific_iou_classes:
|
|
index = specific_iou_classes.index(class_name)
|
|
min_overlap = float(iou_list[index])
|
|
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]
|
|
# colors (OpenCV works with BGR)
|
|
white = (255,255,255)
|
|
light_blue = (255,200,100)
|
|
green = (0,255,0)
|
|
light_red = (30,30,255)
|
|
# 1st line
|
|
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)
|
|
# 2nd line
|
|
v_pos += int(bottom_border / 2.0)
|
|
rank_pos = str(idx+1) # rank position (idx starts at 0)
|
|
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: # if there is intersections between the bounding-boxes
|
|
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)
|
|
# show image
|
|
cv2.imshow("Animation", img)
|
|
cv2.waitKey(20) # show for 20 ms
|
|
# save image to results
|
|
output_img_path = results_files_path + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg"
|
|
cv2.imwrite(output_img_path, img)
|
|
# save the image with all the objects drawn to it
|
|
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[score05_idx]) + " = " + class_name + " F1 "
|
|
Recall_text = "{0:.2f}%".format(rec[score05_idx]*100) + " = " + class_name + " Recall "
|
|
Precision_text = "{0:.2f}%".format(prec[score05_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 not args.quiet:
|
|
if len(prec)>0:
|
|
print(text + "\t||\tscore_threhold=0.5 : " + "F1=" + "{0:.2f}".format(F1[score05_idx])\
|
|
+ " ; Recall=" + "{0:.2f}%".format(rec[score05_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score05_idx]*100))
|
|
else:
|
|
print(text + "\t||\tscore_threhold=0.5 : 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
|
|
|
|
"""
|
|
Draw plot
|
|
"""
|
|
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=0.5")
|
|
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=0.5")
|
|
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=0.5")
|
|
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()
|
|
|
|
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)
|
|
|
|
# remove the temp_files directory
|
|
shutil.rmtree(TEMP_FILES_PATH)
|
|
|
|
"""
|
|
Count total of detection-results
|
|
"""
|
|
# iterate through all the files
|
|
det_counter_per_class = {}
|
|
for txt_file in dr_files_list:
|
|
# get lines to list
|
|
lines_list = file_lines_to_list(txt_file)
|
|
for line in lines_list:
|
|
class_name = line.split()[0]
|
|
# check if class is in the ignore list, if yes skip
|
|
if class_name in args.ignore:
|
|
continue
|
|
# count that object
|
|
if class_name in det_counter_per_class:
|
|
det_counter_per_class[class_name] += 1
|
|
else:
|
|
# if class didn't exist yet
|
|
det_counter_per_class[class_name] = 1
|
|
#print(det_counter_per_class)
|
|
dr_classes = list(det_counter_per_class.keys())
|
|
|
|
|
|
"""
|
|
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,
|
|
'',
|
|
)
|
|
|
|
"""
|
|
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 exists in detection-result but not in ground-truth then there are no true positives in that class
|
|
if class_name not in gt_classes:
|
|
count_true_positives[class_name] = 0
|
|
#print(count_true_positives)
|
|
|
|
"""
|
|
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
|
|
)
|
|
|
|
"""
|
|
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)
|
|
|
|
"""
|
|
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,
|
|
""
|
|
)
|