Create obstruction_detector.py

obstruction_detector.py

@@ -0,0 +1,54 @@
+import cv2 as cv
+import numpy as np
+from scipy.signal import find_peaks
+
+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, img_path):
+        # Load the image
+        img = cv.imread(img_path)
+
+        # 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