File size: 9,127 Bytes
b793f0c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
# Prediction interface for Cog ⚙️
# https://github.com/replicate/cog/blob/main/docs/python.md

import os
import json
from typing import Any
import numpy as np
import random
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
import cv2
import matplotlib.pyplot as plt
from cog import BasePredictor, Input, Path, BaseModel

from subprocess import call

HOME = os.getcwd()
os.chdir("GroundingDINO")
call("pip install -q .", shell=True)
os.chdir(HOME)
os.chdir("segment_anything")
call("pip install -q .", shell=True)
os.chdir(HOME)

# Grounding DINO
import GroundingDINO.groundingdino.datasets.transforms as T
from GroundingDINO.groundingdino.models import build_model
from GroundingDINO.groundingdino.util.slconfig import SLConfig
from GroundingDINO.groundingdino.util.utils import (
    clean_state_dict,
    get_phrases_from_posmap,
)

# segment anything
from segment_anything import build_sam, build_sam_hq, SamPredictor

from ram.models import ram


class ModelOutput(BaseModel):
    tags: str
    rounding_box_img: Path
    masked_img: Path
    json_data: Any


class Predictor(BasePredictor):
    def setup(self):
        """Load the model into memory to make running multiple predictions efficient"""
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        normalize = transforms.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )
        self.image_size = 384
        self.transform = transforms.Compose(
            [
                transforms.Resize((self.image_size, self.image_size)),
                transforms.ToTensor(),
                normalize,
            ]
        )

        # load model
        self.ram_model = ram(
            pretrained="pretrained/ram_swin_large_14m.pth",
            image_size=self.image_size,
            vit="swin_l",
        )
        self.ram_model.eval()
        self.ram_model = self.ram_model.to(self.device)

        self.model = load_model(
            "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py",
            "pretrained/groundingdino_swint_ogc.pth",
            device=self.device,
        )

        self.sam = SamPredictor(
            build_sam(checkpoint="pretrained/sam_vit_h_4b8939.pth").to(self.device)
        )
        self.sam_hq = SamPredictor(
            build_sam_hq(checkpoint="pretrained/sam_hq_vit_h.pth").to(self.device)
        )

    def predict(
        self,
        input_image: Path = Input(description="Input image"),
        use_sam_hq: bool = Input(
            description="Use sam_hq instead of SAM for prediction", default=False
        ),
    ) -> ModelOutput:
        """Run a single prediction on the model"""

        # default settings
        box_threshold = 0.25
        text_threshold = 0.2
        iou_threshold = 0.5

        image_pil, image = load_image(str(input_image))

        raw_image = image_pil.resize((self.image_size, self.image_size))
        raw_image = self.transform(raw_image).unsqueeze(0).to(self.device)

        with torch.no_grad():
            tags, tags_chinese = self.ram_model.generate_tag(raw_image)

        tags = tags[0].replace(" |", ",")

        # run grounding dino model
        boxes_filt, scores, pred_phrases = get_grounding_output(
            self.model, image, tags, box_threshold, text_threshold, device=self.device
        )

        predictor = self.sam_hq if use_sam_hq else self.sam

        image = cv2.imread(str(input_image))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        predictor.set_image(image)

        size = image_pil.size
        H, W = size[1], size[0]
        for i in range(boxes_filt.size(0)):
            boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
            boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
            boxes_filt[i][2:] += boxes_filt[i][:2]

        boxes_filt = boxes_filt.cpu()
        # use NMS to handle overlapped boxes
        print(f"Before NMS: {boxes_filt.shape[0]} boxes")
        nms_idx = (
            torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()
        )
        boxes_filt = boxes_filt[nms_idx]
        pred_phrases = [pred_phrases[idx] for idx in nms_idx]
        print(f"After NMS: {boxes_filt.shape[0]} boxes")

        transformed_boxes = predictor.transform.apply_boxes_torch(
            boxes_filt, image.shape[:2]
        ).to(self.device)

        masks, _, _ = predictor.predict_torch(
            point_coords=None,
            point_labels=None,
            boxes=transformed_boxes.to(self.device),
            multimask_output=False,
        )

        # draw output image
        plt.figure(figsize=(10, 10))
        for mask in masks:
            show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
        for box, label in zip(boxes_filt, pred_phrases):
            show_box(box.numpy(), plt.gca(), label)

        rounding_box_path = "/tmp/automatic_label_output.png"
        plt.axis("off")
        plt.savefig(
            Path(rounding_box_path), bbox_inches="tight", dpi=300, pad_inches=0.0
        )
        plt.close()

        # save masks and json data
        value = 0  # 0 for background
        mask_img = torch.zeros(masks.shape[-2:])
        for idx, mask in enumerate(masks):
            mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1
        plt.figure(figsize=(10, 10))
        plt.imshow(mask_img.numpy())
        plt.axis("off")
        masks_path = "/tmp/mask.png"
        plt.savefig(masks_path, bbox_inches="tight", dpi=300, pad_inches=0.0)
        plt.close()

        json_data = {
            "tags": tags,
            "mask": [{"value": value, "label": "background"}],
        }
        for label, box in zip(pred_phrases, boxes_filt):
            value += 1
            name, logit = label.split("(")
            logit = logit[:-1]  # the last is ')'
            json_data["mask"].append(
                {
                    "value": value,
                    "label": name,
                    "logit": float(logit),
                    "box": box.numpy().tolist(),
                }
            )

        json_path = "/tmp/label.json"
        with open(json_path, "w") as f:
            json.dump(json_data, f)

        return ModelOutput(
            tags=tags,
            masked_img=Path(masks_path),
            rounding_box_img=Path(rounding_box_path),
            json_data=Path(json_path),
        )


def get_grounding_output(
    model, image, caption, box_threshold, text_threshold, device="cpu"
):
    caption = caption.lower()
    caption = caption.strip()
    if not caption.endswith("."):
        caption = caption + "."
    model = model.to(device)
    image = image.to(device)
    with torch.no_grad():
        outputs = model(image[None], captions=[caption])
    logits = outputs["pred_logits"].cpu().sigmoid()[0]  # (nq, 256)
    boxes = outputs["pred_boxes"].cpu()[0]  # (nq, 4)
    logits.shape[0]

    # filter output
    logits_filt = logits.clone()
    boxes_filt = boxes.clone()
    filt_mask = logits_filt.max(dim=1)[0] > box_threshold
    logits_filt = logits_filt[filt_mask]  # num_filt, 256
    boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
    logits_filt.shape[0]

    # get phrase
    tokenlizer = model.tokenizer
    tokenized = tokenlizer(caption)
    # build pred
    pred_phrases = []
    scores = []
    for logit, box in zip(logits_filt, boxes_filt):
        pred_phrase = get_phrases_from_posmap(
            logit > text_threshold, tokenized, tokenlizer
        )
        pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
        scores.append(logit.max().item())

    return boxes_filt, torch.Tensor(scores), pred_phrases


def load_image(image_path):
    # load image
    image_pil = Image.open(image_path).convert("RGB")  # load image

    transform = T.Compose(
        [
            T.RandomResize([800], max_size=1333),
            T.ToTensor(),
            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]
    )
    image, _ = transform(image_pil, None)  # 3, h, w
    return image_pil, image


def load_model(model_config_path, model_checkpoint_path, device):
    args = SLConfig.fromfile(model_config_path)
    args.device = device
    model = build_model(args)
    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
    load_res = model.load_state_dict(
        clean_state_dict(checkpoint["model"]), strict=False
    )
    print(load_res)
    _ = model.eval()
    return model


def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)


def show_box(box, ax, label):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(
        plt.Rectangle((x0, y0), w, h, edgecolor="green", facecolor=(0, 0, 0, 0), lw=1.5)
    )
    ax.text(x0, y0, label)