Delete clipseg/evaluation_utils.py

clipseg/evaluation_utils.py

@@ -1,292 +0,0 @@
-from torch.functional import Tensor
-from general_utils import load_model
-from torch.utils.data import DataLoader
-import torch
-import numpy as np
-
-def denorm(img):
-
-    np_input = False
-    if isinstance(img, np.ndarray):
-        img = torch.from_numpy(img)
-        np_input = True
-
-    mean = torch.Tensor([0.485, 0.456, 0.406])
-    std = torch.Tensor([0.229, 0.224, 0.225])
-
-    img_denorm = (img*std[:,None,None]) + mean[:,None,None]
-
-    if np_input:
-        img_denorm = np.clip(img_denorm.numpy(), 0, 1)
-    else:
-        img_denorm = torch.clamp(img_denorm, 0, 1)
-
-    return img_denorm
-
-
-def norm(img):
-    mean = torch.Tensor([0.485, 0.456, 0.406])
-    std = torch.Tensor([0.229, 0.224, 0.225])
-    return (img - mean[:,None,None]) / std[:,None,None]
-
-
-def fast_iou_curve(p, g):
-    
-    g = g[p.sort().indices]
-    p = torch.sigmoid(p.sort().values)
-    
-    scores = []
-    vals = np.linspace(0, 1, 50)
-
-    for q in vals:
-
-        n = int(len(g) * q)
-
-        valid = torch.where(p > q)[0]
-        if len(valid) > 0:
-            n = int(valid[0])
-        else:
-            n = len(g)
-
-        fn = g[:n].sum()
-        tn = n - fn
-        tp = g[n:].sum()
-        fp = len(g) - n - tp
-
-        iou = tp / (tp + fn + fp)
-
-        precision = tp / (tp + fp)
-        recall = tp / (tp + fn)
-
-        scores += [iou]
-        
-    return vals, scores
-
-
-def fast_rp_curve(p, g):
-    
-    g = g[p.sort().indices]
-    p = torch.sigmoid(p.sort().values)
-    
-    precisions, recalls = [], []
-    vals = np.linspace(p.min(), p.max(), 250)
-
-    for q in p[::100000]:
-
-        n = int(len(g) * q)
-
-        valid = torch.where(p > q)[0]
-        if len(valid) > 0:
-            n = int(valid[0])
-        else:
-            n = len(g)
-
-        fn = g[:n].sum()
-        tn = n - fn
-        tp = g[n:].sum()
-        fp = len(g) - n - tp
-
-        iou = tp / (tp + fn + fp)
-
-        precision = tp / (tp + fp)
-        recall = tp / (tp + fn)
-
-        precisions += [precision]
-        recalls += [recall]
-        
-    return recalls, precisions
-
-
-# Image processing
-
-def img_preprocess(batch, blur=0, grayscale=False, center_context=None, rect=False, rect_color=(255,0,0), rect_width=2, 
-                   brightness=1.0, bg_fac=1, colorize=False, outline=False, image_size=224):
-    import cv2
-
-    rw = rect_width
-
-    out = []
-    for img, mask in zip(batch[1], batch[2]):
-
-        img = img.cpu() if isinstance(img, torch.Tensor) else torch.from_numpy(img)
-        mask = mask.cpu() if isinstance(mask, torch.Tensor) else torch.from_numpy(mask)
-        
-        img *= brightness
-        img_bl = img
-        if blur > 0: # best 5
-            img_bl = torch.from_numpy(cv2.GaussianBlur(img.permute(1,2,0).numpy(), (15, 15), blur)).permute(2,0,1)
-        
-        if grayscale:
-            img_bl = img_bl[1][None]
-        
-        #img_inp = img_ratio*img*mask + (1-img_ratio)*img_bl
-        # img_inp = img_ratio*img*mask + (1-img_ratio)*img_bl * (1-mask)
-        img_inp = img*mask + (bg_fac) * img_bl * (1-mask)
-
-        if rect:
-            _, bbox = crop_mask(img, mask, context=0.1)
-            img_inp[:, bbox[2]: bbox[3], max(0, bbox[0]-rw):bbox[0]+rw] = torch.tensor(rect_color)[:,None,None]
-            img_inp[:, bbox[2]: bbox[3], max(0, bbox[1]-rw):bbox[1]+rw] = torch.tensor(rect_color)[:,None,None]
-            img_inp[:, max(0, bbox[2]-1): bbox[2]+rw, bbox[0]:bbox[1]] = torch.tensor(rect_color)[:,None,None]
-            img_inp[:, max(0, bbox[3]-1): bbox[3]+rw, bbox[0]:bbox[1]] = torch.tensor(rect_color)[:,None,None]
-
-
-        if center_context is not None:
-            img_inp = object_crop(img_inp, mask, context=center_context, image_size=image_size)
-
-        if colorize:
-            img_gray = denorm(img)
-            img_gray = cv2.cvtColor(img_gray.permute(1,2,0).numpy(), cv2.COLOR_RGB2GRAY)
-            img_gray = torch.stack([torch.from_numpy(img_gray)]*3)
-            img_inp = torch.tensor([1,0.2,0.2])[:,None,None] * img_gray * mask + bg_fac * img_gray * (1-mask)
-            img_inp = norm(img_inp)
-
-        if outline:
-            cont = cv2.findContours(mask.byte().numpy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            outline_img = np.zeros(mask.shape, dtype=np.uint8)
-            cv2.drawContours(outline_img, cont[0], -1, thickness=5, color=(255, 255, 255))
-            outline_img = torch.stack([torch.from_numpy(outline_img)]*3).float() / 255.
-            img_inp = torch.tensor([1,0,0])[:,None,None] *  outline_img + denorm(img_inp) * (1- outline_img)
-            img_inp = norm(img_inp)
-
-        out += [img_inp]
-
-    return torch.stack(out)
-
-
-def object_crop(img, mask, context=0.0, square=False, image_size=224):
-    img_crop, bbox = crop_mask(img, mask, context=context, square=square)
-    img_crop = pad_to_square(img_crop, channel_dim=0)
-    img_crop = torch.nn.functional.interpolate(img_crop.unsqueeze(0), (image_size, image_size)).squeeze(0)
-    return img_crop
-    
-
-def crop_mask(img, mask, context=0.0, square=False):
-    
-    assert img.shape[1:] == mask.shape
-    
-    bbox = [mask.max(0).values.argmax(), mask.size(0) - mask.max(0).values.flip(0).argmax()]
-    bbox += [mask.max(1).values.argmax(), mask.size(1) - mask.max(1).values.flip(0).argmax()]
-    bbox = [int(x) for x in bbox]
-    
-    width, height = (bbox[3] - bbox[2]), (bbox[1] - bbox[0])
-
-    # square mask
-    if square:
-        bbox[0] = int(max(0, bbox[0] - context * height))
-        bbox[1] = int(min(mask.size(0), bbox[1] + context * height))
-        bbox[2] = int(max(0, bbox[2] - context * width))
-        bbox[3] = int(min(mask.size(1), bbox[3] + context * width))
-
-        width, height = (bbox[3] - bbox[2]), (bbox[1] - bbox[0])
-        if height > width:
-            bbox[2] = int(max(0, (bbox[2] - 0.5*height)))
-            bbox[3] = bbox[2] + height
-        else:
-            bbox[0] = int(max(0, (bbox[0] - 0.5*width)))
-            bbox[1] = bbox[0] + width
-    else:
-        bbox[0] = int(max(0, bbox[0] - context * height))
-        bbox[1] = int(min(mask.size(0), bbox[1] + context * height))
-        bbox[2] = int(max(0, bbox[2] - context * width))
-        bbox[3] = int(min(mask.size(1), bbox[3] + context * width))
-
-    width, height = (bbox[3] - bbox[2]), (bbox[1] - bbox[0])
-    img_crop = img[:, bbox[2]: bbox[3], bbox[0]: bbox[1]]
-    return img_crop, bbox
-
-
-def pad_to_square(img, channel_dim=2, fill=0):
-    """
-
-
-    add padding such that a squared image is returned """
-    
-    from torchvision.transforms.functional import pad
-
-    if channel_dim == 2:
-        img = img.permute(2, 0, 1)
-    elif channel_dim == 0:
-        pass
-    else:
-        raise ValueError('invalid channel_dim')
-
-    h, w = img.shape[1:]
-    pady1 = pady2 = padx1 = padx2 = 0
-
-    if h > w:
-        padx1 = (h - w) // 2
-        padx2 = h - w - padx1
-    elif w > h:
-        pady1 = (w - h) // 2
-        pady2 = w - h - pady1
-
-    img_padded = pad(img, padding=(padx1, pady1, padx2, pady2), padding_mode='constant')
-
-    if channel_dim == 2:
-        img_padded = img_padded.permute(1, 2, 0)
-
-    return img_padded
-
-
-# qualitative
-
-def split_sentence(inp, limit=9):
-    t_new, current_len = [], 0
-    for k, t in enumerate(inp.split(' ')):
-        current_len += len(t) + 1
-        t_new += [t+' ']
-        # not last
-        if current_len > limit and k != len(inp.split(' ')) - 1:
-            current_len = 0
-            t_new += ['\n']
-
-    t_new = ''.join(t_new)
-    return t_new
-    
-
-from matplotlib import pyplot as plt
-
-
-def plot(imgs, *preds, labels=None, scale=1, cmap=plt.cm.magma, aps=None, gt_labels=None, vmax=None):
-    
-    row_off = 0 if labels is None else 1
-    _, ax = plt.subplots(len(imgs) + row_off, 1 + len(preds), figsize=(scale * float(1 + 2*len(preds)), scale * float(len(imgs)*2)))
-    [a.axis('off') for a in ax.flatten()]
-    
-    if labels is not None:
-        for j in range(len(labels)):
-            t_new = split_sentence(labels[j], limit=6)
-            ax[0, 1+ j].text(0.5, 0.1, t_new, ha='center', fontsize=3+ 10*scale)
-
-
-    for i in range(len(imgs)):
-        ax[i + row_off,0].imshow(imgs[i])
-        for j in range(len(preds)):
-            img = preds[j][i][0].detach().cpu().numpy()
-
-            if gt_labels is not None and labels[j] == gt_labels[i]:
-                print(j, labels[j], gt_labels[i])
-                edgecolor = 'red'
-                if aps is not None:
-                    ax[i + row_off, 1 + j].text(30, 70, f'AP: {aps[i]:.3f}', color='red', fontsize=8)
-            else:
-                edgecolor = 'k'
-
-            rect = plt.Rectangle([0,0], img.shape[0], img.shape[1], facecolor="none", 
-                                 edgecolor=edgecolor, linewidth=3)
-            ax[i + row_off,1 + j].add_patch(rect)
-
-            if vmax is None:
-                this_vmax = 1 
-            elif vmax == 'per_prompt':
-                this_vmax = max([preds[j][_i][0].max() for _i in range(len(imgs))])
-            elif vmax == 'per_image':
-                this_vmax = max([preds[_j][i][0].max() for _j in range(len(preds))])
-
-            ax[i + row_off,1 + j].imshow(img, vmin=0, vmax=this_vmax, cmap=cmap)
-
-    
-            # ax[i,1 + j].imshow(preds[j][i][0].detach().cpu().numpy(), vmin=preds[j].min(), vmax=preds[j].max())
-    plt.tight_layout()
-    plt.subplots_adjust(wspace=0.05, hspace=0.05)