Rename preprocess.py to obstruction_detector.py

obstruction_detector.py

@@ -0,0 +1,55 @@
+import cv2 as cv
+import numpy as np
+from scipy.signal import find_peaks
+from PIL import Image  # Import PIL
+
+class ObstructionDetector:
+    def __init__(self, threshold=500):
+        self.threshold = threshold
+
+    def preprocess_image(self, image):
+        # Convert the image to grayscale if it's a color image
+        if len(image.shape) == 3:
+            image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+
+        # Apply Gaussian blur to reduce noise
+        preprocessed_image = cv.GaussianBlur(image, (5, 5), 0)
+
+        # Perform other preprocessing steps as needed (e.g., contrast adjustment, histogram equalization)
+
+        return preprocessed_image
+
+    def plot_histogram(self, image):
+        # Calculate the histogram
+        histogram = cv.calcHist([image], [0], None, [256], [0, 256])
+
+        # Smoothing the histogram using a simple moving average (window size = 5)
+        kernel = np.ones((5, 1)) / 5
+        smoothed_histogram = cv.filter2D(histogram, -1, kernel)
+
+        return smoothed_histogram
+
+    def count_histogram_peaks(self, smoothed_histogram):
+        # Find peaks in the smoothed histogram with frequency greater than the threshold
+        peaks, _ = find_peaks(smoothed_histogram.flatten(), height=self.threshold)
+        return peaks
+
+    def detect_obstruction(self, pil_image):  # Accept PIL image directly
+        # Convert PIL image to NumPy array
+        img = np.array(pil_image)
+
+        # Preprocess the image
+        preprocessed_img = self.preprocess_image(img)
+
+        # Count the number of peaks in the smoothed histogram above the threshold
+        smoothed_histogram = self.plot_histogram(preprocessed_img)
+        peaks = self.count_histogram_peaks(smoothed_histogram)
+
+        # Check if peaks are too close together
+        peak_spacing = np.diff(peaks)
+        if len(peak_spacing) == 0 or np.all(peak_spacing < 10):
+            report = "A imagem NÃO contém obstrução significativa | e NÃO possui múltiplas distribuições de densidade claramente distintas."
+        else:
+            report = "A imagem contém obstrução significativa | possui múltiplas distribuições de densidade claramente distintas."
+
+        return report

preprocess.py

@@ -1,13 +0,0 @@
-import cv2
-import numpy as np
-
-def unsharp_masking(img, kernel_size=5, threshold=2.0):
-    if kernel_size % 2 == 0:
-        kernel_size += 1  # Ensure the kernel size is odd
-    gaussian = cv2.GaussianBlur(img, (kernel_size, kernel_size), 2.0)
-    unsharp_mask = cv2.addWeighted(img, threshold, gaussian, -1.0, 0)
-    # Clip the pixel values to the valid range [0, 255]
-    unsharp_mask = np.clip(unsharp_mask, 0, 255)
-    # Normalize the image to bring pixel values back to [0, 255]
-    cv2.normalize(unsharp_mask, unsharp_mask, 0, 255, cv2.NORM_MINMAX)
-    return unsharp_mask