Initial commit with app.py

app.py

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+import os
+import cv2
+import numpy as np
+from PIL import Image
+import gradio as gr
+import json
+import matplotlib.pyplot as plt
+import subprocess
+
+repo_url = "https://github.com/CASIA-IVA-Lab/FastSAM.git"
+target_directory = "./FastSAM"
+subprocess.run(['git', 'clone', repo_url, target_directory])
+os.chdir('./FastSAM')
+
+from fastsam import FastSAM, FastSAMPrompt 
+import ast
+import torch
+from PIL import Image
+from utils.tools import convert_box_xywh_to_xyxy
+
+def gradio_fn(pil_input_img):
+    # load model
+    model = FastSAM('./weights/FastSAM.pt')
+    args_point_prompt = ast.literal_eval("[[0,0]]")
+    args_box_prompt = convert_box_xywh_to_xyxy(ast.literal_eval("[[0,0,0,0]]"))
+    args_point_label = ast.literal_eval("[0]")
+    args_text_prompt = None
+    input = pil_input_img
+    input = input.convert("RGB")
+    everything_results = model(
+        input,
+        device="cpu",
+        retina_masks=True,
+        imgsz=1024,
+        conf=0.4,
+        iou=0.9    
+        )
+    bboxes = None
+    points = None
+    point_label = None
+    prompt_process = FastSAMPrompt(input, everything_results, device="cpu")
+    if args_box_prompt[0][2] != 0 and args_box_prompt[0][3] != 0:
+            ann = prompt_process.box_prompt(bboxes=args_box_prompt)
+            bboxes = args_box_prompt
+    elif args_text_prompt != None:
+        ann = prompt_process.text_prompt(text=args_text_prompt)
+    elif args_point_prompt[0] != [0, 0]:
+        ann = prompt_process.point_prompt(
+            points=args_point_prompt, pointlabel=args_point_label
+        )
+        points = args_point_prompt
+        point_label = args_point_label
+    else:
+        ann = prompt_process.everything_prompt()
+    prompt_process.plot(
+        annotations=ann,
+        output_path="./output.jpg",
+        bboxes = bboxes,
+        points = points,
+        point_label = point_label,
+        withContours=False,
+        better_quality=False,
+    )
+    pil_image_output = Image.open('./output.jpg')
+    np_img_array = np.array(pil_image_output)
+    return np_img_array
+
+demo = gr.Interface(fn=gradio_fn, 
+                    inputs=gr.Image(type="pil"), 
+                    outputs="image", 
+                    title="FAST-SAM Segment Everything",
+                    description="- **FastSAM** model that returns segmented RGB image of given input image. \
+                                 - **Credits** : \
+                                    - https://huggingface.co/An-619 \
+                                    - https://github.com/CASIA-IVA-Lab/FastSAM")

requirements.txt

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+# Base-----------------------------------
+matplotlib>=3.2.2
+opencv-python>=4.6.0
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.64.0
+
+pandas>=1.1.4
+seaborn>=0.11.0
+
+gradio==3.35.2
+
+# Ultralytics-----------------------------------
+ultralytics == 8.0.120
+
+# clip----
+git+https://github.com/openai/CLIP.git
+