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# Prediction interface for Cog ⚙️

# https://github.com/replicate/cog/blob/main/docs/python.md

# Thanks for chenxwh.

import argparse

import cv2

import shutil

import ast

from cog import BasePredictor, Input, Path

from ultralytics import YOLO

from utils.tools import *

class Predictor(BasePredictor):

def setup(self):

"""Load the model into memory to make running multiple predictions efficient"""

self.models = {k: YOLO(f"{k}.pt") for k in ["FastSAM-s", "FastSAM-x"]}

def predict(

self,

input_image: Path = Input(description="Input image"),

model_name: str = Input(

description="choose a model",

choices=["FastSAM-x", "FastSAM-s"],

default="FastSAM-x",

),

iou: float = Input(

description="iou threshold for filtering the annotations", default=0.9

),

text_prompt: str = Input(

description='use text prompt eg: "a dog"', default=None

),

conf: float = Input(description="object confidence threshold", default=0.4),

retina: bool = Input(

description="draw high-resolution segmentation masks", default=True

),

box_prompt: str = Input(default="[0,0,0,0]", description="[x,y,w,h]"),

point_prompt: str = Input(default="[[0,0]]", description="[[x1,y1],[x2,y2]]"),

point_label: str = Input(default="[0]", description="[1,0] 0:background, 1:foreground"),

withContours: bool = Input(

description="draw the edges of the masks", default=False

),

better_quality: bool = Input(

description="better quality using morphologyEx", default=False

),

) -> Path:

"""Run a single prediction on the model"""

# default params

out_path = "output"

if os.path.exists(out_path):

shutil.rmtree(out_path)

os.makedirs(out_path, exist_ok=True)

args = argparse.Namespace(

better_quality=better_quality,

box_prompt=box_prompt,

conf=conf,

device=torch.device("cuda"),

img_path=str(input_image),

imgsz=1024,

iou=iou,

model_path="FastSAM-x.pt",

output=out_path,

point_label=point_label,

point_prompt=point_prompt,

randomcolor=True,

retina=retina,

text_prompt=text_prompt,

withContours=withContours,

)

args.point_prompt = ast.literal_eval(args.point_prompt)

args.box_prompt = ast.literal_eval(args.box_prompt)

args.point_label = ast.literal_eval(args.point_label)

model = self.models[model_name]

results = model(

str(input_image),

imgsz=args.imgsz,

device=args.device,

retina_masks=args.retina,

iou=args.iou,

conf=args.conf,

max_det=100,

)

if args.box_prompt[2] != 0 and args.box_prompt[3] != 0:

annotations = prompt(results, args, box=True)

annotations = np.array([annotations])

fast_process(

annotations=annotations,

args=args,

mask_random_color=args.randomcolor,

bbox=convert_box_xywh_to_xyxy(args.box_prompt),

)

elif args.text_prompt != None:

results = format_results(results[0], 0)

annotations = prompt(results, args, text=True)

annotations = np.array([annotations])

fast_process(

annotations=annotations, args=args, mask_random_color=args.randomcolor

)

elif args.point_prompt[0] != [0, 0]:

results = format_results(results[0], 0)

annotations = prompt(results, args, point=True)

# list to numpy

annotations = np.array([annotations])

fast_process(

annotations=annotations,

args=args,

mask_random_color=args.randomcolor,

points=args.point_prompt,

)

else:

fast_process(

annotations=results[0].masks.data,

args=args,

mask_random_color=args.randomcolor,

)

out = "/tmp.out.png"

shutil.copy(os.path.join(out_path, os.listdir(out_path)[0]), out)

return Path(out)

def prompt(results, args, box=None, point=None, text=None):

ori_img = cv2.imread(args.img_path)

ori_h = ori_img.shape[0]

ori_w = ori_img.shape[1]

if box:

mask, idx = box_prompt(

results[0].masks.data,

convert_box_xywh_to_xyxy(args.box_prompt),

ori_h,

ori_w,

)

elif point:

mask, idx = point_prompt(

results, args.point_prompt, args.point_label, ori_h, ori_w

)

elif text:

mask, idx = text_prompt(results, args.text_prompt, args.img_path, args.device)

else:

return None

return mask