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9b33231 | 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 | import streamlit as st
from PIL import Image
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from io import BytesIO
import uuid
import gc
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from segmentation_model import load_model,transform_image, run_inference, save_input_image, save_objects_and_metadata, extract_object
from identification_model import load_yolov8_model, run_object_detection
# from models.text_extraction_model import extract_text
# from models.summarization_model import summarize_text
# from utils.data_mapping import create_summary_table
model = load_model()
detection_model = load_yolov8_model()
def resize_image(image, size=(800, 800)):
return image.resize(size, Image.ANTIALIAS)
def display_masks(outputs, image, threshold=0.5):
masks = outputs[0]['masks']
scores = outputs[0]['scores']
fig, ax = plt.subplots()
ax.imshow(np.array(image))
extracted_objects = []
for i in range(len(scores)):
if scores[i] > threshold:
mask = masks[i].squeeze().cpu().numpy()
mask = np.where(mask > 0.5, 1, 0).astype(np.uint8)
object_img = extract_object(image,mask)
extracted_objects.append(object_img)
#Display the mask
ax.imshow(mask, cmap='jet', alpha=0.5) # Overlay mask on image
st.pyplot(fig)
return extracted_objects
st.title("Image Segmentation with Mask R-CNN and Object Detection")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Convert uploaded file to PIL Image
image = uploaded_file
st.image(image, caption='Uploaded Image.', use_column_width=True)
image = Image.open(uploaded_file).convert('RGB')
# Generate a unique master ID for the image
master_id = str(uuid.uuid4())
# Save the input image
save_input_image(image, master_id)
# Transform image
image_tensor = transform_image(image)
outputs = run_inference(model, image_tensor)
extracted_objects = display_masks(outputs, image)
if extracted_objects:
# Save the extracted objects and their metadata
metadata = save_objects_and_metadata(extracted_objects, master_id)
# Display metadata as a JSON output
st.write("Metadata for extracted objects:")
#st.json(metadata)
# Display each extracted object
st.write("Extracted Objects:")
for i, obj_img in enumerate(extracted_objects):
st.image(obj_img, caption=f'Object {i+1}', use_column_width=True)
# Convert the object image to a numpy array for YOLO inference
obj_img_np = np.array(obj_img)
# Run object detection on each extracted object
detection_results = run_object_detection(detection_model, obj_img_np)
st.write(f"Detection results for Object {i+1}:")
st.json(detection_results)
else:
st.write("No objects were detected")
# del extracted_objects
# gc.collect()
# Display results
#display_masks(outputs, image)
if uploaded_file is not None:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption='Uploaded Image.', use_column_width=True)
image_tensor = transform_image(image)
outputs = run_inference(model, image_tensor)
display_masks(outputs, image)
# def upload_image():
# uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# if uploaded_file is not None:
# image = Image.open(uploaded_file)
# return image
# return None
# # def display_segmentation(image):
# # st.image(image, caption="Original Image", use_column_width=True)
# # Transform and run inference
# # image_tensor = transform_image(image)
# # outputs = run_inference(image_tensor)
# # # Save segmented objects
# # output_dir = 'segmented_objects/'
# # save_segmented_objects(image, outputs, output_dir)
# # segmented_images = [Image.open(f"{output_dir}object_{i+1}.png") for i in range(len(outputs[0]['scores']))]
# # for img in segmented_images:
# # st.image(img, caption="Segmented Object", use_column_width=True)
# def main():
# st.title("Image Processing Pipeline")
# # uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png"])
# # if uploaded_file:
# # image_path = f"data/input_images/{uploaded_file.name}"
# # image = Image.open(uploaded_file)
# # image.save(image_path) # Save the uploaded image for further processing
# # st.image(image, caption="Uploaded Image")
# # if st.button("Segment Image"):
# # segmented = segment_image(image_path)
# # st.image(segmented, caption="Segmented Image", use_column_width=True)
# # if st.button("Identify and Extract Objects"):
# # objects_data = identify_and_extract_objects(image_path)
# # extracted_objects = []
# # for obj_data in objects_data:
# # object_image = Image.open(obj_data['Image Path'])
# # text = extract_text(object_image)
# # summary = summarize_text(text)
# # obj_data['Text'] = text
# # obj_data['Summary'] = summary
# # extracted_objects.append(obj_data)
# # st.image(object_image, caption=f"Object {obj_data['ID']} - Label {obj_data['Label']}")
# # summary_file = create_summary_table(extracted_objects)
# # st.write(pd.DataFrame(extracted_objects))
# # st.download_button(label="Download Summary Table", data=open(summary_file).read(), file_name="summary.csv")
# if __name__ == "__main__":
# main() |