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559 lines
24 KiB
559 lines
24 KiB
# Plotting utils
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import glob
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import math
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import os
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import random
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from copy import copy
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from pathlib import Path
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import cv2
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import torch
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import yaml
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from scipy.signal import butter, filtfilt
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from PIL import Image, ImageDraw, ImageFont
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# from utils.general import xywh2xyxy, xyxy2xywh
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from utils.metrics import fitness
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from utils.general import ( check_requirements, clip_coords, user_config_dir, is_ascii, is_chinese,
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xywh2xyxy, xyxy2xywh)
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# Settings
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CONFIG_DIR = user_config_dir() # Ultralytics settings dir
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RANK = int(os.getenv('RANK', -1))
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matplotlib.rc('font', **{'size': 11})
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matplotlib.use('Agg') # for writing to files only
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb('#' + c) for c in hex]
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@staticmethod
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def hex2rgb(h): # rgb order (PIL)
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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colors = Colors() # create instance for 'from utils.plots import colors'
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def check_font(font='Arial.ttf', size=10):
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# Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
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font = Path(font)
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if font.exists()==False:
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print(font,"不存在")
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return
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# font = font if font.exists() else (CONFIG_DIR / font.name)
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try:
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return ImageFont.truetype(font if font.exists() else font.name, size)
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except Exception as e: # download if missing
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# url = "https://ultralytics.com/assets/" + font.name
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# print(f'Downloading {url} to {font}...')
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# torch.hub.download_url_to_file(url, str(font), progress=False)
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# try:
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# return ImageFont.truetype(str(font), size)
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# except TypeError:
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# check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
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print(e)
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# 输出中文
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font_cn = ImageFont.truetype("../Font/simhei.ttf", 30, encoding="utf-8")
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class Annotator:
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# if RANK in (-1, 0):
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# check_font() # download TTF if necessary
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# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
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def __init__(self, im, line_width=1, font_size=30, pil=False, example='abc'):
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assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
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# self.im = im
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self.pil = pil or not is_ascii(example) or is_chinese(example)
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if self.pil: # use PIL
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
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self.draw = ImageDraw.Draw(self.im)
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self.font= font_cn
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# try:
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# self.font = ImageFont.truetype("../Font/simhei.ttf", font_size, encoding="utf-8")
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# except Exception as e: # download if missing
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# print(e)
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else: # use cv2
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self.im = im
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self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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# def cv2DrawCNText(self, text, shape, textColor=(0, 255, 0), textSize=30):
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# # 图像从OpenCV格式转换成PIL格式
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# if (isinstance(self.im, np.ndarray)): # 判断是否OpenCV图片类型
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# img = Image.fromarray(cv2.cvtColor(self.im, cv2.COLOR_BGR2RGB))
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# draw = ImageDraw.Draw(img)
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# # fontText = ImageFont.truetype("../Font/simhei.ttf", textSize, encoding="utf-8")
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# draw.text(shape, text, textColor, font=self.font)
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# self.im= cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
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def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
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# Add one xyxy box to image with label
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if self.pil or not is_ascii(label):
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self.draw.rectangle([box[0],box[1],box[2],box[3]], outline=color) # box
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if label:
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w, h = self.font.getsize(label) # text width, height
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outside = box[1] - h >= 0 # label fits outside box
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self.draw.rectangle([box[0],
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box[1] - h if outside else box[1],
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box[0] + w + 1,
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box[1] + 1 if outside else box[1] + h + 1], fill=color)
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# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
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self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
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else: # cv2
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p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
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cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
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if label:
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tf = max(self.lw - 1, 1) # font thickness
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w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
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outside = p1[1] - h - 3 >= 0 # label fits outside box
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p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
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cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
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thickness=tf, lineType=cv2.LINE_AA)
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def drawcount(self, countlist,names, color=(128, 128, 128), txt_color=(240,10, 10)):
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row =0
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tf = max(self.lw - 1, 1) # font thickness
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for modex in countlist:
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draw_text_postion = (10, int(20+row*30))
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label =names[modex["key"]]+" 上行:"+str(modex["up"])+": 下行:"+str(modex["down"])
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if self.pil:
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self.draw.text(draw_text_postion, label, fill=txt_color, font=self.font)
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else:
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cv2.putText(self.im, label, draw_text_postion, 0, self.lw / 2, txt_color,
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thickness=2, lineType=cv2.LINE_AA)
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row+=1
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def cvimg(self):
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if self.pil:
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return cv2.cvtColor(np.asarray(self.im), cv2.COLOR_RGB2BGR)
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else:
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return self.im
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def rectangle(self, xy, fill=None, outline=None, width=1):
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# Add rectangle to image (PIL-only)
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self.draw.rectangle(xy, fill, outline, width)
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def text(self, xy, text, txt_color=(255, 255, 255)):
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# Add text to image (PIL-only)
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w, h = self.font.getsize(text) # text width, height
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self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
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def result(self):
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# Return annotated image as array
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return np.asarray(self.im)
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def color_list():
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# Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
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def hex2rgb(h):
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']]
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def hist2d(x, y, n=100):
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# 2d histogram used in labels.png and evolve.png
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xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
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hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
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xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
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yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
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return np.log(hist[xidx, yidx])
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def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
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# https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
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def butter_lowpass(cutoff, fs, order):
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nyq = 0.5 * fs
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normal_cutoff = cutoff / nyq
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return butter(order, normal_cutoff, btype='low', analog=False)
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b, a = butter_lowpass(cutoff, fs, order=order)
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return filtfilt(b, a, data) # forward-backward filter
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
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def plot_wh_methods(): # from utils.plots import *; plot_wh_methods()
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# Compares the two methods for width-height anchor multiplication
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# https://github.com/ultralytics/yolov3/issues/168
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x = np.arange(-4.0, 4.0, .1)
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ya = np.exp(x)
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yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
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fig = plt.figure(figsize=(6, 3), tight_layout=True)
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plt.plot(x, ya, '.-', label='YOLOv3')
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plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2')
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plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6')
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plt.xlim(left=-4, right=4)
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plt.ylim(bottom=0, top=6)
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plt.xlabel('input')
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plt.ylabel('output')
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plt.grid()
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plt.legend()
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fig.savefig('comparison.png', dpi=200)
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def output_to_target(output):
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# Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
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targets = []
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for i, o in enumerate(output):
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for *box, conf, cls in o.cpu().numpy():
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targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
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return np.array(targets)
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def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
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# Plot image grid with labels
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if isinstance(images, torch.Tensor):
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images = images.cpu().float().numpy()
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if isinstance(targets, torch.Tensor):
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targets = targets.cpu().numpy()
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# un-normalise
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if np.max(images[0]) <= 1:
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images *= 255
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tl = 3 # line thickness
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tf = max(tl - 1, 1) # font thickness
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bs, _, h, w = images.shape # batch size, _, height, width
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bs = min(bs, max_subplots) # limit plot images
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ns = np.ceil(bs ** 0.5) # number of subplots (square)
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# Check if we should resize
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scale_factor = max_size / max(h, w)
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if scale_factor < 1:
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h = math.ceil(scale_factor * h)
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w = math.ceil(scale_factor * w)
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colors = color_list() # list of colors
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mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
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for i, img in enumerate(images):
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if i == max_subplots: # if last batch has fewer images than we expect
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break
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block_x = int(w * (i // ns))
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block_y = int(h * (i % ns))
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img = img.transpose(1, 2, 0)
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if scale_factor < 1:
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img = cv2.resize(img, (w, h))
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mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
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if len(targets) > 0:
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image_targets = targets[targets[:, 0] == i]
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boxes = xywh2xyxy(image_targets[:, 2:6]).T
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classes = image_targets[:, 1].astype('int')
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labels = image_targets.shape[1] == 6 # labels if no conf column
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conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred)
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if boxes.shape[1]:
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if boxes.max() <= 1.01: # if normalized with tolerance 0.01
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boxes[[0, 2]] *= w # scale to pixels
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boxes[[1, 3]] *= h
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elif scale_factor < 1: # absolute coords need scale if image scales
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boxes *= scale_factor
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boxes[[0, 2]] += block_x
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boxes[[1, 3]] += block_y
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for j, box in enumerate(boxes.T):
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cls = int(classes[j])
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color = colors[cls % len(colors)]
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cls = names[cls] if names else cls
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
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plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)
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# Draw image filename labels
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if paths:
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label = Path(paths[i]).name[:40] # trim to 40 char
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
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lineType=cv2.LINE_AA)
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# Image border
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cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
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if fname:
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r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size
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mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
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# cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save
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Image.fromarray(mosaic).save(fname) # PIL save
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return mosaic
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def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
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# Plot LR simulating training for full epochs
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optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
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y = []
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for _ in range(epochs):
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scheduler.step()
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y.append(optimizer.param_groups[0]['lr'])
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plt.plot(y, '.-', label='LR')
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plt.xlabel('epoch')
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plt.ylabel('LR')
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plt.grid()
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plt.xlim(0, epochs)
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plt.ylim(0)
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plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
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plt.close()
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def plot_test_txt(): # from utils.plots import *; plot_test()
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# Plot test.txt histograms
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x = np.loadtxt('test.txt', dtype=np.float32)
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box = xyxy2xywh(x[:, :4])
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cx, cy = box[:, 0], box[:, 1]
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fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
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ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
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ax.set_aspect('equal')
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plt.savefig('hist2d.png', dpi=300)
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fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
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ax[0].hist(cx, bins=600)
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ax[1].hist(cy, bins=600)
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plt.savefig('hist1d.png', dpi=200)
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def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
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# Plot targets.txt histograms
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x = np.loadtxt('targets.txt', dtype=np.float32).T
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s = ['x targets', 'y targets', 'width targets', 'height targets']
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fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
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ax = ax.ravel()
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for i in range(4):
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ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
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ax[i].legend()
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ax[i].set_title(s[i])
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plt.savefig('targets.jpg', dpi=200)
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def plot_study_txt(path='study/', x=None): # from utils.plots import *; plot_study_txt()
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# Plot study.txt generated by test.py
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fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
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ax = ax.ravel()
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fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
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for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]:
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y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
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x = np.arange(y.shape[1]) if x is None else np.array(x)
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s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
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for i in range(7):
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ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
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ax[i].set_title(s[i])
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j = y[3].argmax() + 1
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ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
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label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
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ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
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'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
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|
|
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ax2.grid()
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ax2.set_xlim(0, 30)
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|
ax2.set_ylim(29, 51)
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ax2.set_yticks(np.arange(30, 55, 5))
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ax2.set_xlabel('GPU Speed (ms/img)')
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|
ax2.set_ylabel('COCO AP val')
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|
ax2.legend(loc='lower right')
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plt.savefig('test_study.png', dpi=300)
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|
|
|
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def plot_labels(labels, save_dir=Path(''), loggers=None):
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# plot dataset labels
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print('Plotting labels... ')
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c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
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|
nc = int(c.max() + 1) # number of classes
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|
colors = color_list()
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|
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
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|
|
|
# seaborn correlogram
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|
sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
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plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
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|
plt.close()
|
|
|
|
# matplotlib labels
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|
matplotlib.use('svg') # faster
|
|
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
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|
ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
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|
ax[0].set_xlabel('classes')
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|
sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
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|
sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
|
|
|
|
# rectangles
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|
labels[:, 1:3] = 0.5 # center
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|
labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
|
|
img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
|
|
for cls, *box in labels[:1000]:
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|
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot
|
|
ax[1].imshow(img)
|
|
ax[1].axis('off')
|
|
|
|
for a in [0, 1, 2, 3]:
|
|
for s in ['top', 'right', 'left', 'bottom']:
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|
ax[a].spines[s].set_visible(False)
|
|
|
|
plt.savefig(save_dir / 'labels.jpg', dpi=200)
|
|
matplotlib.use('Agg')
|
|
plt.close()
|
|
|
|
# loggers
|
|
for k, v in loggers.items() or {}:
|
|
if k == 'wandb' and v:
|
|
v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]})
|
|
|
|
|
|
def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution()
|
|
# Plot hyperparameter evolution results in evolve.txt
|
|
with open(yaml_file) as f:
|
|
hyp = yaml.load(f, Loader=yaml.FullLoader)
|
|
x = np.loadtxt('evolve.txt', ndmin=2)
|
|
f = fitness(x)
|
|
# weights = (f - f.min()) ** 2 # for weighted results
|
|
plt.figure(figsize=(10, 12), tight_layout=True)
|
|
matplotlib.rc('font', **{'size': 8})
|
|
for i, (k, v) in enumerate(hyp.items()):
|
|
y = x[:, i + 7]
|
|
# mu = (y * weights).sum() / weights.sum() # best weighted result
|
|
mu = y[f.argmax()] # best single result
|
|
plt.subplot(6, 5, i + 1)
|
|
plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
|
|
plt.plot(mu, f.max(), 'k+', markersize=15)
|
|
plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters
|
|
if i % 5 != 0:
|
|
plt.yticks([])
|
|
print('%15s: %.3g' % (k, mu))
|
|
plt.savefig('evolve.png', dpi=200)
|
|
print('\nPlot saved as evolve.png')
|
|
|
|
|
|
def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
|
|
# Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
|
|
ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
|
|
s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
|
|
files = list(Path(save_dir).glob('frames*.txt'))
|
|
for fi, f in enumerate(files):
|
|
try:
|
|
results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
|
|
n = results.shape[1] # number of rows
|
|
x = np.arange(start, min(stop, n) if stop else n)
|
|
results = results[:, x]
|
|
t = (results[0] - results[0].min()) # set t0=0s
|
|
results[0] = x
|
|
for i, a in enumerate(ax):
|
|
if i < len(results):
|
|
label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
|
|
a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
|
|
a.set_title(s[i])
|
|
a.set_xlabel('time (s)')
|
|
# if fi == len(files) - 1:
|
|
# a.set_ylim(bottom=0)
|
|
for side in ['top', 'right']:
|
|
a.spines[side].set_visible(False)
|
|
else:
|
|
a.remove()
|
|
except Exception as e:
|
|
print('Warning: Plotting error for %s; %s' % (f, e))
|
|
|
|
ax[1].legend()
|
|
plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
|
|
|
|
|
|
def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay()
|
|
# Plot training 'results*.txt', overlaying train and val losses
|
|
s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends
|
|
t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
|
|
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
|
|
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
|
|
n = results.shape[1] # number of rows
|
|
x = range(start, min(stop, n) if stop else n)
|
|
fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
|
|
ax = ax.ravel()
|
|
for i in range(5):
|
|
for j in [i, i + 5]:
|
|
y = results[j, x]
|
|
ax[i].plot(x, y, marker='.', label=s[j])
|
|
# y_smooth = butter_lowpass_filtfilt(y)
|
|
# ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
|
|
|
|
ax[i].set_title(t[i])
|
|
ax[i].legend()
|
|
ax[i].set_ylabel(f) if i == 0 else None # add filename
|
|
fig.savefig(f.replace('.txt', '.png'), dpi=200)
|
|
|
|
|
|
def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
|
|
# Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
|
|
fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
|
|
ax = ax.ravel()
|
|
s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
|
|
'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
|
|
if bucket:
|
|
# files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
|
|
files = ['results%g.txt' % x for x in id]
|
|
c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
|
|
os.system(c)
|
|
else:
|
|
files = list(Path(save_dir).glob('results*.txt'))
|
|
assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
|
|
for fi, f in enumerate(files):
|
|
try:
|
|
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
|
|
n = results.shape[1] # number of rows
|
|
x = range(start, min(stop, n) if stop else n)
|
|
for i in range(10):
|
|
y = results[i, x]
|
|
if i in [0, 1, 2, 5, 6, 7]:
|
|
y[y == 0] = np.nan # don't show zero loss values
|
|
# y /= y[0] # normalize
|
|
label = labels[fi] if len(labels) else f.stem
|
|
ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
|
|
ax[i].set_title(s[i])
|
|
# if i in [5, 6, 7]: # share train and val loss y axes
|
|
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
|
except Exception as e:
|
|
print('Warning: Plotting error for %s; %s' % (f, e))
|
|
|
|
ax[1].legend()
|
|
fig.savefig(Path(save_dir) / 'results.png', dpi=200)
|
|
|