--- a
+++ b/validate.py
@@ -0,0 +1,154 @@
+# import pydicom
+# import numpy as np
+# from PIL import Image
+#
+# tolerance = 1
+#
+# # Load the DICOM file
+# dicom = pydicom.dcmread(r"/home/ubuntu/nlp_project/Code/physionet.org/files/mimic-cxr/2.1.0/files/p10/p10000032/s50414267/02aa804e-bde0afdd-112c0b34-7bc16630-4e384014.dcm")
+# dicom_pixels = dicom.pixel_array
+#
+# # Load the PNG file
+# png_image = Image.open(r"/home/ubuntu/nlp_project/Code/physionet.org/files/mimic-cxr/2.1.0/files/p10/p10000032/s50414267/out_png/02aa804e-bde0afdd-112c0b34-7bc16630-4e384014.png")
+# png_pixels = np.array(png_image)
+#
+# # Compare shapes
+# assert dicom_pixels.shape == png_pixels.shape, "Shapes of DICOM and PNG do not match"
+#
+# # Compare pixel values
+# difference = np.abs(dicom_pixels - png_pixels)
+#
+# dicom_non_zero_elements = dicom_pixels[dicom_pixels != 0]
+# dicom_non_zero_count = len(dicom_non_zero_elements)
+#
+# # Get non-zero elements in PNG array
+# png_non_zero_elements = png_pixels[png_pixels != 0]
+# png_non_zero_count = len(png_non_zero_elements)
+#
+# print(f"Non-zero elements in DICOM: {dicom_non_zero_count}")
+# print(f"Non-zero elements in PNG: {png_non_zero_count}")
+#
+# # If needed, print the actual non-zero elements (be cautious for large arrays)
+# print(f"Non-zero elements in DICOM array:\n{dicom_non_zero_elements}")
+# print(f"Non-zero elements in PNG array:\n{png_non_zero_elements}")
+#
+#
+# print(f"First 10 non-zero elements in DICOM array:\n{dicom_non_zero_elements[:10]}")
+# print(f"First 10 non-zero elements in PNG array:\n{png_non_zero_elements[:10]}")
+#
+#
+# # assert np.all(difference < tolerance), f"Pixel difference exceeds tolerance: {difference.max()}"
+
+
+import os
+import pandas as pd
+from tqdm import tqdm
+import pydicom
+import numpy as np
+from PIL import Image
+
+
+# Function to convert DICOM to PNG
+def convert_dicom_to_png(dicom_path, output_path):
+    try:
+        # Read the DICOM file
+        dicom = pydicom.dcmread(dicom_path)
+        # Get pixel array
+        pixel_array = dicom.pixel_array
+        # Normalize pixel values to 0-255
+        pixel_array = ((pixel_array - pixel_array.min()) / (pixel_array.max() - pixel_array.min()) * 255).astype(
+            np.uint8)
+        # Save as PNG
+        image = Image.fromarray(pixel_array)
+        image.save(output_path)
+    except Exception as e:
+        print(f"Error converting {dicom_path} to PNG: {e}")
+
+
+# Function to extract findings and impressions from a report
+def extract_findings_and_impression(file_path):
+    with open(file_path, 'r') as file:
+        content = file.read()
+
+    # Extract Findings
+    findings_start = content.find("FINDINGS:")
+    impression_start = content.find("IMPRESSION:")
+
+    findings = ""
+    impression = ""
+
+    if findings_start != -1:
+        findings = content[findings_start + len("FINDINGS:"):impression_start].strip()
+
+    if impression_start != -1:
+        impression = content[impression_start + len("IMPRESSION:"):].strip()
+
+    return findings, impression
+
+
+# Main logic to create the DataFrame
+reports_root_path = input("Enter the root path for reports: ").strip()
+
+# Ensure the path exists
+if not os.path.exists(reports_root_path):
+    raise FileNotFoundError(f"The specified path does not exist: {reports_root_path}")
+
+data = []
+
+grp_folders = os.listdir(reports_root_path)
+
+for p_grp in grp_folders:
+    cxr_path = os.path.join(reports_root_path, p_grp)
+    p_files = os.listdir(cxr_path)
+
+    for p in p_files:
+        res_path = os.path.join(cxr_path, p)
+
+        if os.path.isdir(res_path):
+            dicom_dirs = [d for d in os.listdir(res_path) if os.path.isdir(os.path.join(res_path, d))]
+            txt_files = [f for f in os.listdir(res_path) if f.endswith('.txt') and f.startswith('s')]
+
+            for dicom_dir in dicom_dirs:
+                dicom_path = os.path.join(res_path, dicom_dir)
+                dicom_files = [os.path.join(dicom_path, f) for f in os.listdir(dicom_path) if f.endswith('.dcm')]
+
+                report_file = f"{dicom_dir}.txt"
+                if report_file in txt_files:
+                    report_path = os.path.join(res_path, report_file)
+                    findings, impressions = extract_findings_and_impression(report_path)
+
+                    for dicom_file in dicom_files:
+                        dicom_id = os.path.basename(dicom_file)
+                        png_path = dicom_file.replace('.dcm', '.png')  # Define the PNG output path
+
+                        # Convert the DICOM to PNG
+                        convert_dicom_to_png(dicom_file, png_path)
+
+                        # Append data to the list
+                        # data.append({
+                        #     "dicom_path": dicom_file,
+                        #     "png_path": png_path,
+                        #     "dicom_id": dicom_id,
+                        #     "findings": findings,
+                        #     "impressions": impressions
+                        # })
+
+                        data_entry = {
+                            "dicom_path": dicom_file,
+                            "png_path": png_path,
+                            "dicom_id": dicom_id,
+                            "findings": findings,
+                            "impressions": impressions
+                        }
+
+                        data.append(data_entry)
+
+                        print(f"Processed PNG path: {data_entry['png_path']}")
+
+df = pd.DataFrame(data)
+
+print(df.head())
+print(f"Total entries: {len(df)}")
+
+# Save the DataFrame to a CSV file
+df.to_csv('data_with_png_paths.csv', index=False)