Delete utils/plots.py

utils/plots.py

@@ -1,89 +0,0 @@
-import random
-
-import cv2
-import matplotlib
-import numpy as np
-import torch
-
-
-def show_seg_result(img, result, index=0, epoch=0, batch=0, save_dir=None, is_ll=False, palette=None, is_demo=False,
-                    is_gt=False):
-    if palette is None:
-        palette = np.random.randint(0, 255, size=(3, 3))
-    palette[0] = [0, 0, 0]
-    palette[1] = [0, 255, 0]
-    palette[2] = [255, 0, 0]
-    palette = np.array(palette)
-    assert palette.shape[0] == 3  # len(classes)
-    assert palette.shape[1] == 3
-    assert len(palette.shape) == 2
-
-    if not is_demo:
-        color_seg = np.zeros((result.shape[0], result.shape[1], 3), dtype=np.uint8)
-        for label, color in enumerate(palette):
-            color_seg[result == label, :] = color
-    else:
-        color_area = np.zeros((result[0].shape[0], result[0].shape[1], 3), dtype=np.uint8)
-
-        color_area[result[0] == 1] = [0, 255, 0]
-        color_area[result[1] == 1] = [255, 0, 0]
-        color_seg = color_area
-
-    # convert to BGR
-    color_seg = color_seg[..., ::-1]
-    # print(color_seg.shape)
-    color_mask = np.mean(color_seg, 2)
-    img[color_mask != 0] = img[color_mask != 0] * 0.5 + color_seg[color_mask != 0] * 0.5
-    # img = img * 0.5 + color_seg * 0.5
-    img = img.astype(np.uint8)
-    img = cv2.resize(img, (1280, 720), interpolation=cv2.INTER_LINEAR)
-
-    if not is_demo:
-        if not is_gt:
-            if not is_ll:
-                cv2.imwrite(save_dir + "/batch_{}_{}_{}_da_segresult.png".format(epoch, batch, index), img)
-            else:
-                cv2.imwrite(save_dir + "/batch_{}_{}_{}_ll_segresult.png".format(epoch, batch, index), img)
-        else:
-            if not is_ll:
-                cv2.imwrite(save_dir + "/batch_{}_{}_{}_da_seg_gt.png".format(epoch, batch, index), img)
-            else:
-                cv2.imwrite(save_dir + "/batch_{}_{}_{}_ll_seg_gt.png".format(epoch, batch, index), img)
-    return img
-
-
-def plot_one_box(x, img, color=None, label=None, line_thickness=3):
-    # Plots one bounding box on image img
-    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
-    color = color if color else [random.randint(0, 255) for _ in range(3)]
-    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
-    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
-    if label:
-        tf = max(tl - 1, 1)  # font thickness
-        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
-        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA)
-
-
-def driving_area_mask(da_seg_out, width, height, pad_w, pad_h, ratio):
-    da_predict = da_seg_out[:, :, pad_h:(height - pad_h), pad_w:(width - pad_w)]
-    da_seg_mask = torch.nn.functional.interpolate(da_predict, scale_factor=int(1 / ratio), mode='bilinear')
-    _, da_seg_mask = torch.max(da_seg_mask, 1)
-    da_seg_mask = da_seg_mask.int().squeeze().cpu().numpy()
-    return da_seg_mask
-
-
-def lane_line_mask(ll_seg_out, width, height, pad_w, pad_h, ratio):
-    ll_predict = ll_seg_out[:, :, pad_h:(height - pad_h), pad_w:(width - pad_w)]
-    ll_seg_mask = torch.nn.functional.interpolate(ll_predict, scale_factor=int(1 / ratio), mode='bilinear')
-    _, ll_seg_mask = torch.max(ll_seg_mask, 1)
-    ll_seg_mask = ll_seg_mask.int().squeeze().cpu().numpy()
-    return ll_seg_mask
-
-def color_list():
-    # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
-    def hex2rgb(h):
-        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
-
-    return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()]  # or BASE_ (8), CSS4_ (148), XKCD_ (949)