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import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import numpy as np
import re
from landingai.common import decode_bitmap_rle
import os # For accessing secrets
ENDPOINT_ID = "ba678fa4-65d1-4b87-8c85-cebd15224783"
API_KEY = os.environ.get("land_sk_ikq7WEKGtaKI7pXIcKt2x7RoyYE6FBReqGOmKtEhjcmFbLbQsK")
API_URL = f"https://predict.app.landing.ai/inference/v1/predict?endpoint_id={ENDPOINT_ID}"
def predict_from_landinglens(image_path):
# Load and keep original image
original_img = Image.open(image_path).convert("RGB")
img_array = np.array(original_img)
# Get image dimensions
height, width = img_array.shape[:2]
total_pixels = height * width
# Prepare for API
buffered = BytesIO()
original_img.save(buffered, format="JPEG")
img_bytes = buffered.getvalue()
files = {"file": (image_path, img_bytes, "image/jpeg")}
headers = {"apikey": API_KEY}
try:
response = requests.post(API_URL, files=files, headers=headers)
if response.status_code == 503:
return "Service temporarily unavailable. Please try again later."
response.raise_for_status()
prediction = response.json()
if "predictions" not in prediction or not prediction.get("predictions"):
print("No 'predictions' key found or it's empty.")
return "Error: No 'predictions' found."
bitmaps = prediction["predictions"]["bitmaps"]
masked_images = []
coverage_info = []
for i, (bitmap_id, bitmap_data) in enumerate(bitmaps.items()):
try:
# Decode mask
mask = decode_bitmap_rle(bitmap_data["bitmap"])
if isinstance(mask, list):
mask = np.array(mask)
# Reshape mask to match image dimensions
mask = mask.reshape(prediction["predictions"]["imageHeight"],
prediction["predictions"]["imageWidth"])
# Calculate area coverage
mask_area = np.sum(mask > 0)
coverage_percentage = (mask_area / total_pixels) * 100
label_name = bitmap_data.get("label_name", f"Mask {i}")
coverage_info.append(f"{label_name}: {coverage_percentage:.2f}%")
# Create colored overlay using Pillow
mask_image = Image.fromarray((mask > 0).astype(np.uint8) * 255)
overlay = Image.new("RGB", mask_image.size, (255, 0, 0)) # Red overlay
mask_image = mask_image.convert("RGB")
masked_image = Image.blend(original_img, overlay, alpha=0.5 * mask_image.convert("L").point(lambda x: x > 0 and 1))
masked_images.append(masked_image)
except Exception as e:
print(f"Error processing mask {i}: {e}")
continue
return masked_images, "\n".join(coverage_info)
except requests.exceptions.RequestException as e:
print(f"API Error: {e}")
return f"API Error: {e}"
iface = gr.Interface(
fn=predict_from_landinglens,
inputs=gr.Image(type="filepath"),
outputs=[
gr.Gallery(format="png"),
gr.Textbox(label="Area of each mask in the image")
],
title="Crosswalk detection model",
)
iface.launch() |