ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -33,15 +33,55 @@ class Colors:
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def __init__(self):
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"""Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
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hexs = ('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(f'#{c}') for c in hexs]
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hexs = (
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"FF3838",
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"FF9D97",
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"FF701F",
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"FFB21D",
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"CFD231",
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"48F90A",
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"92CC17",
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"3DDB86",
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"1A9334",
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"00D4BB",
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"2C99A8",
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"00C2FF",
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"344593",
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"6473FF",
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"0018EC",
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"8438FF",
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"520085",
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"CB38FF",
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"FF95C8",
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"FF37C7",
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)
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self.palette = [self.hex2rgb(f"#{c}") for c in hexs]
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self.n = len(self.palette)
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self.pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
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[153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
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[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
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[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]],
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dtype=np.uint8)
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self.pose_palette = np.array(
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[
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[255, 128, 0],
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[255, 153, 51],
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[255, 178, 102],
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[230, 230, 0],
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[255, 153, 255],
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[153, 204, 255],
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[255, 102, 255],
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[255, 51, 255],
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[102, 178, 255],
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[51, 153, 255],
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[255, 153, 153],
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[255, 102, 102],
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[255, 51, 51],
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[153, 255, 153],
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[102, 255, 102],
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[51, 255, 51],
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[0, 255, 0],
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[0, 0, 255],
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[255, 0, 0],
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[255, 255, 255],
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],
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dtype=np.uint8,
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)
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def __call__(self, i, bgr=False):
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"""Converts hex color codes to RGB values."""
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@ -51,7 +91,7 @@ class Colors:
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@staticmethod
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def hex2rgb(h):
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"""Converts hex color codes to RGB values (i.e. default PIL order)."""
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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 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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@ -71,9 +111,9 @@ class Annotator:
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kpt_color (List[int]): Color palette for keypoints.
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"""
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def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
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def __init__(self, im, line_width=None, font_size=None, font="Arial.ttf", pil=False, example="abc"):
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"""Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
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assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
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assert im.data.contiguous, "Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images."
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non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
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self.pil = pil or non_ascii
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self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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@ -81,26 +121,45 @@ class Annotator:
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
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self.draw = ImageDraw.Draw(self.im)
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try:
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font = check_font('Arial.Unicode.ttf' if non_ascii else font)
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font = check_font("Arial.Unicode.ttf" if non_ascii else font)
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size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
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self.font = ImageFont.truetype(str(font), size)
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except Exception:
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self.font = ImageFont.load_default()
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# Deprecation fix for w, h = getsize(string) -> _, _, w, h = getbox(string)
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if check_version(pil_version, '9.2.0'):
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if check_version(pil_version, "9.2.0"):
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self.font.getsize = lambda x: self.font.getbbox(x)[2:4] # text width, height
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else: # use cv2
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self.im = im if im.flags.writeable else im.copy()
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self.tf = max(self.lw - 1, 1) # font thickness
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self.sf = self.lw / 3 # font scale
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# Pose
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self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
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[8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
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self.skeleton = [
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[16, 14],
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[14, 12],
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[17, 15],
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[15, 13],
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[12, 13],
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[6, 12],
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[7, 13],
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[6, 7],
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[6, 8],
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[7, 9],
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[8, 10],
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[9, 11],
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[2, 3],
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[1, 2],
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[1, 3],
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[2, 4],
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[3, 5],
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[4, 6],
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[5, 7],
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]
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self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
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self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
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def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
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def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
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"""Add one xyxy box to image with label."""
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if isinstance(box, torch.Tensor):
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box = box.tolist()
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@ -134,13 +193,16 @@ class Annotator:
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outside = p1[1] - h >= 3
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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,
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label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
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0,
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self.sf,
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txt_color,
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thickness=self.tf,
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lineType=cv2.LINE_AA)
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cv2.putText(
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self.im,
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label,
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(p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
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0,
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self.sf,
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txt_color,
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thickness=self.tf,
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lineType=cv2.LINE_AA,
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)
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def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
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"""
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@ -171,7 +233,7 @@ class Annotator:
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im_gpu = im_gpu.flip(dims=[0]) # flip channel
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im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
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im_gpu = im_gpu * inv_alpha_masks[-1] + mcs
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im_mask = (im_gpu * 255)
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im_mask = im_gpu * 255
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im_mask_np = im_mask.byte().cpu().numpy()
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self.im[:] = im_mask_np if retina_masks else ops.scale_image(im_mask_np, self.im.shape)
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if self.pil:
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@ -230,9 +292,9 @@ class Annotator:
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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), anchor='top', box_style=False):
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def text(self, xy, text, txt_color=(255, 255, 255), anchor="top", box_style=False):
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"""Adds text to an image using PIL or cv2."""
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if anchor == 'bottom': # start y from font bottom
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if anchor == "bottom": # start y from font bottom
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w, h = self.font.getsize(text) # text width, height
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xy[1] += 1 - h
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if self.pil:
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@ -241,8 +303,8 @@ class Annotator:
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self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
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# Using `txt_color` for background and draw fg with white color
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txt_color = (255, 255, 255)
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if '\n' in text:
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lines = text.split('\n')
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if "\n" in text:
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lines = text.split("\n")
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_, h = self.font.getsize(text)
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for line in lines:
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self.draw.text(xy, line, fill=txt_color, font=self.font)
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@ -314,15 +376,12 @@ class Annotator:
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text_y = t_size_in[1]
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# Create a rounded rectangle for in_count
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cv2.rectangle(self.im, (text_x - 5, text_y - 5), (text_x + text_width + 7, text_y + t_size_in[1] + 7), color,
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-1)
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cv2.putText(self.im,
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str(counts), (text_x, text_y + t_size_in[1]),
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0,
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tl / 2,
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txt_color,
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self.tf,
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lineType=cv2.LINE_AA)
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cv2.rectangle(
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self.im, (text_x - 5, text_y - 5), (text_x + text_width + 7, text_y + t_size_in[1] + 7), color, -1
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)
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cv2.putText(
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self.im, str(counts), (text_x, text_y + t_size_in[1]), 0, tl / 2, txt_color, self.tf, lineType=cv2.LINE_AA
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)
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@staticmethod
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def estimate_pose_angle(a, b, c):
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@ -375,7 +434,7 @@ class Annotator:
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center_kpt (int): centroid pose index for workout monitoring
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line_thickness (int): thickness for text display
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"""
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angle_text, count_text, stage_text = (f' {angle_text:.2f}', 'Steps : ' + f'{count_text}', f' {stage_text}')
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angle_text, count_text, stage_text = (f" {angle_text:.2f}", "Steps : " + f"{count_text}", f" {stage_text}")
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font_scale = 0.6 + (line_thickness / 10.0)
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# Draw angle
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@ -383,21 +442,37 @@ class Annotator:
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angle_text_position = (int(center_kpt[0]), int(center_kpt[1]))
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angle_background_position = (angle_text_position[0], angle_text_position[1] - angle_text_height - 5)
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angle_background_size = (angle_text_width + 2 * 5, angle_text_height + 2 * 5 + (line_thickness * 2))
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cv2.rectangle(self.im, angle_background_position, (angle_background_position[0] + angle_background_size[0],
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angle_background_position[1] + angle_background_size[1]),
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(255, 255, 255), -1)
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cv2.rectangle(
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self.im,
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angle_background_position,
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(
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angle_background_position[0] + angle_background_size[0],
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angle_background_position[1] + angle_background_size[1],
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),
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(255, 255, 255),
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-1,
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)
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cv2.putText(self.im, angle_text, angle_text_position, 0, font_scale, (0, 0, 0), line_thickness)
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# Draw Counts
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(count_text_width, count_text_height), _ = cv2.getTextSize(count_text, 0, font_scale, line_thickness)
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count_text_position = (angle_text_position[0], angle_text_position[1] + angle_text_height + 20)
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count_background_position = (angle_background_position[0],
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angle_background_position[1] + angle_background_size[1] + 5)
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count_background_position = (
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angle_background_position[0],
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angle_background_position[1] + angle_background_size[1] + 5,
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)
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count_background_size = (count_text_width + 10, count_text_height + 10 + (line_thickness * 2))
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cv2.rectangle(self.im, count_background_position, (count_background_position[0] + count_background_size[0],
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count_background_position[1] + count_background_size[1]),
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(255, 255, 255), -1)
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cv2.rectangle(
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self.im,
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count_background_position,
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(
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count_background_position[0] + count_background_size[0],
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count_background_position[1] + count_background_size[1],
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),
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(255, 255, 255),
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-1,
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)
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cv2.putText(self.im, count_text, count_text_position, 0, font_scale, (0, 0, 0), line_thickness)
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# Draw Stage
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@ -406,9 +481,16 @@ class Annotator:
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stage_background_position = (stage_text_position[0], stage_text_position[1] - stage_text_height - 5)
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stage_background_size = (stage_text_width + 10, stage_text_height + 10)
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cv2.rectangle(self.im, stage_background_position, (stage_background_position[0] + stage_background_size[0],
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stage_background_position[1] + stage_background_size[1]),
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(255, 255, 255), -1)
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cv2.rectangle(
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self.im,
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stage_background_position,
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(
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stage_background_position[0] + stage_background_size[0],
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stage_background_position[1] + stage_background_size[1],
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),
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(255, 255, 255),
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-1,
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)
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cv2.putText(self.im, stage_text, stage_text_position, 0, font_scale, (0, 0, 0), line_thickness)
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def seg_bbox(self, mask, mask_color=(255, 0, 255), det_label=None, track_label=None):
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@ -423,14 +505,20 @@ class Annotator:
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"""
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cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
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label = f'Track ID: {track_label}' if track_label else det_label
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label = f"Track ID: {track_label}" if track_label else det_label
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text_size, _ = cv2.getTextSize(label, 0, 0.7, 1)
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cv2.rectangle(self.im, (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
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(int(mask[0][0]) + text_size[0] // 2 + 5, int(mask[0][1] + 5)), mask_color, -1)
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cv2.rectangle(
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self.im,
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(int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
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(int(mask[0][0]) + text_size[0] // 2 + 5, int(mask[0][1] + 5)),
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mask_color,
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-1,
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)
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cv2.putText(self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255),
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2)
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cv2.putText(
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self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255), 2
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)
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def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255), thickness=2, pins_radius=10):
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"""
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@ -452,24 +540,24 @@ class Annotator:
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@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
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@plt_settings()
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def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
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def plot_labels(boxes, cls, names=(), save_dir=Path(""), on_plot=None):
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"""Plot training labels including class histograms and box statistics."""
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import pandas as pd
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import seaborn as sn
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# Filter matplotlib>=3.7.2 warning and Seaborn use_inf and is_categorical FutureWarnings
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warnings.filterwarnings('ignore', category=UserWarning, message='The figure layout has changed to tight')
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings("ignore", category=UserWarning, message="The figure layout has changed to tight")
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warnings.filterwarnings("ignore", category=FutureWarning)
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# Plot dataset labels
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LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
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nc = int(cls.max() + 1) # number of classes
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boxes = boxes[:1000000] # limit to 1M boxes
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x = pd.DataFrame(boxes, columns=['x', 'y', 'width', 'height'])
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x = pd.DataFrame(boxes, columns=["x", "y", "width", "height"])
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# Seaborn correlogram
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sn.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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sn.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()
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# Matplotlib labels
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@ -477,14 +565,14 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
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y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
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for i in range(nc):
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y[2].patches[i].set_color([x / 255 for x in colors(i)])
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ax[0].set_ylabel('instances')
|
||||
ax[0].set_ylabel("instances")
|
||||
if 0 < len(names) < 30:
|
||||
ax[0].set_xticks(range(len(names)))
|
||||
ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
|
||||
else:
|
||||
ax[0].set_xlabel('classes')
|
||||
sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
|
||||
sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
|
||||
ax[0].set_xlabel("classes")
|
||||
sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
|
||||
sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9)
|
||||
|
||||
# Rectangles
|
||||
boxes[:, 0:2] = 0.5 # center
|
||||
|
|
@ -493,20 +581,20 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
|
|||
for cls, box in zip(cls[:500], boxes[:500]):
|
||||
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
|
||||
ax[1].imshow(img)
|
||||
ax[1].axis('off')
|
||||
ax[1].axis("off")
|
||||
|
||||
for a in [0, 1, 2, 3]:
|
||||
for s in ['top', 'right', 'left', 'bottom']:
|
||||
for s in ["top", "right", "left", "bottom"]:
|
||||
ax[a].spines[s].set_visible(False)
|
||||
|
||||
fname = save_dir / 'labels.jpg'
|
||||
fname = save_dir / "labels.jpg"
|
||||
plt.savefig(fname, dpi=200)
|
||||
plt.close()
|
||||
if on_plot:
|
||||
on_plot(fname)
|
||||
|
||||
|
||||
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
|
||||
def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True):
|
||||
"""
|
||||
Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop.
|
||||
|
||||
|
|
@ -545,29 +633,31 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
|
|||
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
|
||||
xyxy = ops.xywh2xyxy(b).long()
|
||||
xyxy = ops.clip_boxes(xyxy, im.shape)
|
||||
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
|
||||
crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)]
|
||||
if save:
|
||||
file.parent.mkdir(parents=True, exist_ok=True) # make directory
|
||||
f = str(increment_path(file).with_suffix('.jpg'))
|
||||
f = str(increment_path(file).with_suffix(".jpg"))
|
||||
# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
|
||||
Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
|
||||
return crop
|
||||
|
||||
|
||||
@threaded
|
||||
def plot_images(images,
|
||||
batch_idx,
|
||||
cls,
|
||||
bboxes=np.zeros(0, dtype=np.float32),
|
||||
confs=None,
|
||||
masks=np.zeros(0, dtype=np.uint8),
|
||||
kpts=np.zeros((0, 51), dtype=np.float32),
|
||||
paths=None,
|
||||
fname='images.jpg',
|
||||
names=None,
|
||||
on_plot=None,
|
||||
max_subplots=16,
|
||||
save=True):
|
||||
def plot_images(
|
||||
images,
|
||||
batch_idx,
|
||||
cls,
|
||||
bboxes=np.zeros(0, dtype=np.float32),
|
||||
confs=None,
|
||||
masks=np.zeros(0, dtype=np.uint8),
|
||||
kpts=np.zeros((0, 51), dtype=np.float32),
|
||||
paths=None,
|
||||
fname="images.jpg",
|
||||
names=None,
|
||||
on_plot=None,
|
||||
max_subplots=16,
|
||||
save=True,
|
||||
):
|
||||
"""Plot image grid with labels."""
|
||||
if isinstance(images, torch.Tensor):
|
||||
images = images.cpu().float().numpy()
|
||||
|
|
@ -585,7 +675,7 @@ def plot_images(images,
|
|||
max_size = 1920 # max image size
|
||||
bs, _, h, w = images.shape # batch size, _, height, width
|
||||
bs = min(bs, max_subplots) # limit plot images
|
||||
ns = np.ceil(bs ** 0.5) # number of subplots (square)
|
||||
ns = np.ceil(bs**0.5) # number of subplots (square)
|
||||
if np.max(images[0]) <= 1:
|
||||
images *= 255 # de-normalise (optional)
|
||||
|
||||
|
|
@ -593,7 +683,7 @@ def plot_images(images,
|
|||
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
|
||||
for i in range(bs):
|
||||
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
|
||||
mosaic[y:y + h, x:x + w, :] = images[i].transpose(1, 2, 0)
|
||||
mosaic[y : y + h, x : x + w, :] = images[i].transpose(1, 2, 0)
|
||||
|
||||
# Resize (optional)
|
||||
scale = max_size / ns / max(h, w)
|
||||
|
|
@ -612,7 +702,7 @@ def plot_images(images,
|
|||
annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
|
||||
if len(cls) > 0:
|
||||
idx = batch_idx == i
|
||||
classes = cls[idx].astype('int')
|
||||
classes = cls[idx].astype("int")
|
||||
labels = confs is None
|
||||
|
||||
if len(bboxes):
|
||||
|
|
@ -633,14 +723,14 @@ def plot_images(images,
|
|||
color = colors(c)
|
||||
c = names.get(c, c) if names else c
|
||||
if labels or conf[j] > 0.25: # 0.25 conf thresh
|
||||
label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
|
||||
label = f"{c}" if labels else f"{c} {conf[j]:.1f}"
|
||||
annotator.box_label(box, label, color=color, rotated=is_obb)
|
||||
|
||||
elif len(classes):
|
||||
for c in classes:
|
||||
color = colors(c)
|
||||
c = names.get(c, c) if names else c
|
||||
annotator.text((x, y), f'{c}', txt_color=color, box_style=True)
|
||||
annotator.text((x, y), f"{c}", txt_color=color, box_style=True)
|
||||
|
||||
# Plot keypoints
|
||||
if len(kpts):
|
||||
|
|
@ -680,7 +770,9 @@ def plot_images(images,
|
|||
else:
|
||||
mask = image_masks[j].astype(bool)
|
||||
with contextlib.suppress(Exception):
|
||||
im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
|
||||
im[y : y + h, x : x + w, :][mask] = (
|
||||
im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
|
||||
)
|
||||
annotator.fromarray(im)
|
||||
if save:
|
||||
annotator.im.save(fname) # save
|
||||
|
|
@ -691,7 +783,7 @@ def plot_images(images,
|
|||
|
||||
|
||||
@plt_settings()
|
||||
def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None):
|
||||
def plot_results(file="path/to/results.csv", dir="", segment=False, pose=False, classify=False, on_plot=None):
|
||||
"""
|
||||
Plot training results from a results CSV file. The function supports various types of data including segmentation,
|
||||
pose estimation, and classification. Plots are saved as 'results.png' in the directory where the CSV is located.
|
||||
|
|
@ -714,6 +806,7 @@ def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False,
|
|||
"""
|
||||
import pandas as pd
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
|
||||
save_dir = Path(file).parent if file else Path(dir)
|
||||
if classify:
|
||||
fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True)
|
||||
|
|
@ -728,32 +821,32 @@ def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False,
|
|||
fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
|
||||
index = [1, 2, 3, 4, 5, 8, 9, 10, 6, 7]
|
||||
ax = ax.ravel()
|
||||
files = list(save_dir.glob('results*.csv'))
|
||||
assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
|
||||
files = list(save_dir.glob("results*.csv"))
|
||||
assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
|
||||
for f in files:
|
||||
try:
|
||||
data = pd.read_csv(f)
|
||||
s = [x.strip() for x in data.columns]
|
||||
x = data.values[:, 0]
|
||||
for i, j in enumerate(index):
|
||||
y = data.values[:, j].astype('float')
|
||||
y = data.values[:, j].astype("float")
|
||||
# y[y == 0] = np.nan # don't show zero values
|
||||
ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) # actual results
|
||||
ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2) # smoothing line
|
||||
ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8) # actual results
|
||||
ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2) # smoothing line
|
||||
ax[i].set_title(s[j], fontsize=12)
|
||||
# if j in [8, 9, 10]: # share train and val loss y axes
|
||||
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING: Plotting error for {f}: {e}')
|
||||
LOGGER.warning(f"WARNING: Plotting error for {f}: {e}")
|
||||
ax[1].legend()
|
||||
fname = save_dir / 'results.png'
|
||||
fname = save_dir / "results.png"
|
||||
fig.savefig(fname, dpi=200)
|
||||
plt.close()
|
||||
if on_plot:
|
||||
on_plot(fname)
|
||||
|
||||
|
||||
def plt_color_scatter(v, f, bins=20, cmap='viridis', alpha=0.8, edgecolors='none'):
|
||||
def plt_color_scatter(v, f, bins=20, cmap="viridis", alpha=0.8, edgecolors="none"):
|
||||
"""
|
||||
Plots a scatter plot with points colored based on a 2D histogram.
|
||||
|
||||
|
|
@ -774,14 +867,18 @@ def plt_color_scatter(v, f, bins=20, cmap='viridis', alpha=0.8, edgecolors='none
|
|||
# Calculate 2D histogram and corresponding colors
|
||||
hist, xedges, yedges = np.histogram2d(v, f, bins=bins)
|
||||
colors = [
|
||||
hist[min(np.digitize(v[i], xedges, right=True) - 1, hist.shape[0] - 1),
|
||||
min(np.digitize(f[i], yedges, right=True) - 1, hist.shape[1] - 1)] for i in range(len(v))]
|
||||
hist[
|
||||
min(np.digitize(v[i], xedges, right=True) - 1, hist.shape[0] - 1),
|
||||
min(np.digitize(f[i], yedges, right=True) - 1, hist.shape[1] - 1),
|
||||
]
|
||||
for i in range(len(v))
|
||||
]
|
||||
|
||||
# Scatter plot
|
||||
plt.scatter(v, f, c=colors, cmap=cmap, alpha=alpha, edgecolors=edgecolors)
|
||||
|
||||
|
||||
def plot_tune_results(csv_file='tune_results.csv'):
|
||||
def plot_tune_results(csv_file="tune_results.csv"):
|
||||
"""
|
||||
Plot the evolution results stored in an 'tune_results.csv' file. The function generates a scatter plot for each key
|
||||
in the CSV, color-coded based on fitness scores. The best-performing configurations are highlighted on the plots.
|
||||
|
|
@ -810,33 +907,33 @@ def plot_tune_results(csv_file='tune_results.csv'):
|
|||
v = x[:, i + num_metrics_columns]
|
||||
mu = v[j] # best single result
|
||||
plt.subplot(n, n, i + 1)
|
||||
plt_color_scatter(v, fitness, cmap='viridis', alpha=.8, edgecolors='none')
|
||||
plt.plot(mu, fitness.max(), 'k+', markersize=15)
|
||||
plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
|
||||
plt.tick_params(axis='both', labelsize=8) # Set axis label size to 8
|
||||
plt_color_scatter(v, fitness, cmap="viridis", alpha=0.8, edgecolors="none")
|
||||
plt.plot(mu, fitness.max(), "k+", markersize=15)
|
||||
plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters
|
||||
plt.tick_params(axis="both", labelsize=8) # Set axis label size to 8
|
||||
if i % n != 0:
|
||||
plt.yticks([])
|
||||
|
||||
file = csv_file.with_name('tune_scatter_plots.png') # filename
|
||||
file = csv_file.with_name("tune_scatter_plots.png") # filename
|
||||
plt.savefig(file, dpi=200)
|
||||
plt.close()
|
||||
LOGGER.info(f'Saved {file}')
|
||||
LOGGER.info(f"Saved {file}")
|
||||
|
||||
# Fitness vs iteration
|
||||
x = range(1, len(fitness) + 1)
|
||||
plt.figure(figsize=(10, 6), tight_layout=True)
|
||||
plt.plot(x, fitness, marker='o', linestyle='none', label='fitness')
|
||||
plt.plot(x, gaussian_filter1d(fitness, sigma=3), ':', label='smoothed', linewidth=2) # smoothing line
|
||||
plt.title('Fitness vs Iteration')
|
||||
plt.xlabel('Iteration')
|
||||
plt.ylabel('Fitness')
|
||||
plt.plot(x, fitness, marker="o", linestyle="none", label="fitness")
|
||||
plt.plot(x, gaussian_filter1d(fitness, sigma=3), ":", label="smoothed", linewidth=2) # smoothing line
|
||||
plt.title("Fitness vs Iteration")
|
||||
plt.xlabel("Iteration")
|
||||
plt.ylabel("Fitness")
|
||||
plt.grid(True)
|
||||
plt.legend()
|
||||
|
||||
file = csv_file.with_name('tune_fitness.png') # filename
|
||||
file = csv_file.with_name("tune_fitness.png") # filename
|
||||
plt.savefig(file, dpi=200)
|
||||
plt.close()
|
||||
LOGGER.info(f'Saved {file}')
|
||||
LOGGER.info(f"Saved {file}")
|
||||
|
||||
|
||||
def output_to_target(output, max_det=300):
|
||||
|
|
@ -861,7 +958,7 @@ def output_to_rotated_target(output, max_det=300):
|
|||
return targets[:, 0], targets[:, 1], targets[:, 2:-1], targets[:, -1]
|
||||
|
||||
|
||||
def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
|
||||
def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")):
|
||||
"""
|
||||
Visualize feature maps of a given model module during inference.
|
||||
|
||||
|
|
@ -872,7 +969,7 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
|
|||
n (int, optional): Maximum number of feature maps to plot. Defaults to 32.
|
||||
save_dir (Path, optional): Directory to save results. Defaults to Path('runs/detect/exp').
|
||||
"""
|
||||
for m in ['Detect', 'Pose', 'Segment']:
|
||||
for m in ["Detect", "Pose", "Segment"]:
|
||||
if m in module_type:
|
||||
return
|
||||
batch, channels, height, width = x.shape # batch, channels, height, width
|
||||
|
|
@ -886,9 +983,9 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
|
|||
plt.subplots_adjust(wspace=0.05, hspace=0.05)
|
||||
for i in range(n):
|
||||
ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
|
||||
ax[i].axis('off')
|
||||
ax[i].axis("off")
|
||||
|
||||
LOGGER.info(f'Saving {f}... ({n}/{channels})')
|
||||
plt.savefig(f, dpi=300, bbox_inches='tight')
|
||||
LOGGER.info(f"Saving {f}... ({n}/{channels})")
|
||||
plt.savefig(f, dpi=300, bbox_inches="tight")
|
||||
plt.close()
|
||||
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
|
||||
np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy()) # npy save
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue