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from typing import List
import gradio as gr
import numpy as np
import supervision as sv
import torch
from PIL import Image
from transformers import pipeline, CLIPProcessor, CLIPModel
#************
#Variables globales
MARKDOWN = """
#SAM
"""
EXAMPLES = [
["https://media.roboflow.com/notebooks/examples/dog.jpeg", "dog", 0.5],
["https://media.roboflow.com/notebooks/examples/dog.jpeg", "building", 0.5],
["https://media.roboflow.com/notebooks/examples/dog-3.jpeg", "jacket", 0.5],
["https://media.roboflow.com/notebooks/examples/dog-3.jpeg", "coffee", 0.6],
]
MIN_AREA_THRESHOLD = 0.01
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
SAM_GENERATOR = pipeline(
task = "mask-generation",
model = "facebook/sam-vit-large",
device = DEVICE
)
SEMITRANSPARENT_MASK_ANNOTATOR = sv.MaskAnnotator(
color = sv.Color.red(),
color_lookup = sv.ColorLookup.INDEX
)
SOLID_MASK_ANNOTATOR = sv.MaskAnnotator(
color = sv.Color.white(),
color_lookup = sv.ColorLookup.INDEX,
opacity = 1
)
#************
#funciones de trabajo
def run_sam(image_rgb_pil : Image.Image ) -> sv.Detections:
outputs = SAM_GENERATOR(image_rgb_pil, points_per_batch = 32)
mask = np.array(outputs['masks'])
return sv.Detections(xyxy=sv.mask_to_xyxy(masks=mask), mask=mask)
def reverse_mask_image(image: np.ndarray, mask: np.ndarray, gray_value=128):
gray_color = np.array([
gray_value,
gray_value,
gray_value
], dtype=np.uint8)
return np.where(mask[..., None], image, gray_color)
"""
def filter_detections(image_rgb_pil: Image.Image, detections: sv.Detections) -> sv.Detections:
img_rgb_numpy = np.array(image_rgb_pil)
filtering_mask = []
for xyxy, mask in zip(detections.xyxy, detections.mask):
crop = sv.crop_image(
image = img_rgb_numpy,
xyxy =xyxy
)
mask_crop = sv.crop_image(
image=mask,
xyxy=xyxy
)
masked_crop = reverse_mask_image(
image=crop,
mask=mask_crop
)
filtering_mask = np.array(
filtering_mask
)
return detections[filtering_mask]
"""
def inference (image_rgb_pil: Image.Image) -> List[Image.Image]:
width, height = image_rgb_pil.size
area = width * height
detections = run_sam(
image_rgb_pil
)
detections = detections[ detections.area /area > MIN_AREA_THRESHOLD ]
#detections = filter_detections(
# image_rgb_pil=image_rgb_pil,
# detections=detections,
#)
blank_image = Image.new("RGB", (width, height), "black")
return [
annotate(
image_rgb_pil=image_rgb_pil,
detections=detections,
annotator=SEMITRANSPARENT_MASK_ANNOTATOR),
annotate(
image_rgb_pil=blank_image,
detections=detections,
annotator=SOLID_MASK_ANNOTATOR)
]
#************
#GRADIO CONSTRUCTION
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
with gr.Row():
with gr.Column():
input_image = gr.Image(
image_mode = 'RGB',
type = 'pil',
height = 500
)
submit_button = gr.Button("Pruébalo!!!")
gallery = gr.Gallery(
label = "Result",
object_fit = "scale-down",
preview = True
)
with gr.Row():
gr.Examples(
examples = EXAMPLES,
fn = inference,
inputs = [
input_image
],
outputs = [gallery],
cache_examples = False,
run_on_click = True
)
submit_button.click(
inference,
inputs = [
input_image
],
outputs = gallery
)
demo.launch( debug = True, show_error = True ) |