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import cv2
import torch
import matplotlib
import tkinter as tk
import numpy as np
import matplotlib.pyplot as plt
from tkinter import filedialog, messagebox
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.widgets import RectangleSelector
crop_coords = None
pixel_size_um = None
def calculate_pixel_size(image, image_size_um=2.0):
"""Calculate the pixel size in micrometers based on the image dimensions."""
image_width_pixels = image.shape[1]
pixel_size_um = image_size_um / image_width_pixels
return pixel_size_um
def process_image(data, height, width, pixel_size_um: float = 2.0) -> dict:
"""
Process a 2D material with shape: [H, W]
Returns
---
- Dictionary of material properties
"""
# Calculate pixel size
pixel_size_um = calculate_pixel_size(data)
# Normalize data
normalized_data = cv2.normalize(data, None, 0, 255, cv2.NORM_MINMAX).astype(
np.uint8
)
# Ensure the image is in float format for processing
img = data.astype(np.float32)
h, w = data.shape
# Flattening: Fit a 1st-order polynomial (linear) to each row and subtract
flattened_img = np.zeros_like(img, dtype=np.float32)
for i in range(h):
row = img[i, :]
x = np.arange(w)
# Fit a linear polynomial (1st order)
p = np.polyfit(x, row, 1) # Fit line y = ax + b
# Compute background
background = np.polyval(p, x)
# Subtract the background from the row
flattened_img[i, :] = row - background
# flattened_img[i, :] -= np.min(flattened_img[i, :]) # Shift to keep variations
# Normalize the image for display
flattened_img = cv2.normalize(flattened_img, None, 0, 255, cv2.NORM_MINMAX)
flattened_img = np.uint8(flattened_img)
# Calculate the average current
average_current = np.mean(data) / 1e-9
# Define the coverage threshold
threshold = 120 * 1e-12
# Create a binary mask for coverage (1 = covered, 0 = uncovered)
coverage_mask = (data > threshold).astype(int)
# Calculate coverage percentage
coverage_percentage = (np.sum(coverage_mask) / coverage_mask.size) * 100
# Create a color map: Light blue for coverage, light grey for uncovered
colors = ["black", "mistyrose"]
cmap = LinearSegmentedColormap.from_list("coverage_map", colors, N=2)
# Plot the coverage map
x = np.linspace(0, 2, data.shape[1]) # X-axis (microns)
y = np.linspace(0, 2, data.shape[0]) # Y-axis (microns)
X, Y = np.meshgrid(x, y)
# Apply Gamma Correction
gamma = 0.7
gamma_corrected_img = np.power(flattened_img / 255.0, gamma) * 255.0
gamma_corrected_img = gamma_corrected_img.astype(np.uint8)
# Apply Bilateral Filter
bilateral_filtered = cv2.bilateralFilter(gamma_corrected_img, 9, 30, 75)
# Apply Gaussian Blur
blurred = cv2.GaussianBlur(bilateral_filtered, (3, 3), 3)
# Adaptive Thresholding
thresholded = cv2.adaptiveThreshold(
blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 7, 1
)
# Contour Detection
contours, _ = cv2.findContours(
thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
)
output_image = cv2.cvtColor(blurred, cv2.COLOR_GRAY2BGR)
curves_length = 0
circular_shapes_area = 0
extended_shapes_area = 0
circular_shapes = []
curved_lines = []
extended_shapes = []
for contour in contours:
if cv2.contourArea(contour) > 0:
perimeter = cv2.arcLength(contour, closed=True)
area = cv2.contourArea(contour)
circularity = 4 * np.pi * area / (perimeter**2) if perimeter != 0 else 0
# Create a mask for the contour
mask = np.zeros_like(thresholded, dtype=np.uint8)
cv2.drawContours(mask, [contour], -1, 255, -1)
# Check if the contour is inside a covered area (valid contour)
if not np.any(coverage_mask[mask == 255]):
continue # Skip this contour if it falls in an uncovered region
# Calculate the total number of pixels inside the contour
total_pixels = cv2.countNonZero(mask)
# Calculate the number of white pixels inside the contour
white_pixels = cv2.countNonZero(cv2.bitwise_and(mask, thresholded))
# Calculate the number of dark pixels inside the contour (where the thresholded image is 0)
dark_pixels = total_pixels - cv2.countNonZero(
cv2.bitwise_and(mask, thresholded)
)
x, y, w, h = cv2.boundingRect(contour)
aspect_ratio = float(w) / h
if circularity > 0.2 and area <= 1000:
# Check for white pixel coverage only for circular shapes
# if dark_pixels / total_pixels >= 0.8:
# continue # Skip this contour if 80% or more of the pixels are white
# Process circular shapes
circular_shapes.append(contour)
circular_shapes_area += area
cv2.drawContours(output_image, [contour], -1, (0, 255, 255), 1)
elif aspect_ratio > 2 and area <= 1000:
# Check for white pixel coverage only for extended shapes
# if total_pixels > 0 and (white_pixels / total_pixels) >= 0.8:
# continue # Skip this contour if 80% or more of the pixels are white
# Process extended shapes
extended_shapes.append(contour)
extended_shapes_area += area
cv2.drawContours(output_image, [contour], -1, (0, 255, 255), 1)
else:
# First, calculate the area of the contour
contour_area = cv2.contourArea(contour)
if contour_area <= 250: # Threshold for small areas
extended_shapes.append(contour)
extended_shapes_area += contour_area
cv2.drawContours(output_image, [contour], -1, (0, 255, 255), 1)
else:
# Process curved lines without the white pixel check
curved_lines.append(contour)
curve_length = cv2.arcLength(contour, closed=True)
curves_length += curve_length
cv2.drawContours(output_image, [contour], -1, (0, 255, 0), 1)
# Convert areas and lengths to micrometers
circular_shapes_area_um2 = circular_shapes_area * (pixel_size_um**2)
extended_shapes_area_um2 = extended_shapes_area * (pixel_size_um**2)
curves_length_um = curves_length * pixel_size_um
Total_Defect_Area = circular_shapes_area_um2 + extended_shapes_area_um2
Total_Defect_Percentage = 100 * Total_Defect_Area / (height * width)
results = {
"average_current": average_current,
"coverage_percentage": coverage_percentage,
"circular_shapes_area": circular_shapes_area_um2,
"extended_shapes_area": extended_shapes_area_um2,
"curves_length": curves_length_um,
"total_defect_area": Total_Defect_Area,
"total_defect_percentage": Total_Defect_Percentage,
"number_ext_shapes": len(extended_shapes),
}
return results
def calculate_roughness(height_data: np.ndarray) -> dict:
if isinstance(height_data, torch.Tensor):
height_data = height_data.cpu().numpy()
# Zero-centering the data
height_data -= np.mean(height_data)
# RMS roughness in nm
Sq = np.sqrt(np.mean(height_data**2)) * 1e9
# Mean roughness in nm
Sa = np.mean(np.abs(height_data)) * 1e9
return {"RMS roughness (Sq)": Sq, "Mean roughness (Sa)": Sa}
def pcnt_abs_diff_surface_roughness(x1: torch.Tensor, x2: torch.Tensor) -> dict:
x1 = x1.cpu().numpy()
x2 = x2.cpu().numpy()
char_1 = calculate_roughness(x1)
char_2 = calculate_roughness(x2)
diffs = {}
for k in char_1:
v1, v2 = char_1[k], char_2[k]
err = abs(v1 - v2)
diffs[k] = err
return diffs
def calculate_abs_diff_between_samples(
x1: torch.Tensor, x2: torch.Tensor, image_size_um: float
) -> dict:
x1 = x1.cpu().numpy()
x2 = x2.cpu().numpy()
char_1 = process_image(x1, x1.shape[0], x1.shape[1], image_size_um)
char_2 = process_image(x2, x2.shape[1], x2.shape[1], image_size_um)
diffs = {}
for k in char_1:
v1, v2 = char_1[k], char_2[k]
err = abs(v1 - v2)
diffs[k] = err
return diffs
if __name__ == "__main__":
dummy = np.random.random((128, 128))
res = process_image(dummy, 128, 128)
breakpoint()
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