init

find.py

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+import cv2
+import numpy as np
+import joblib
+from skimage.feature import hog
+import imutils
+from imutils.object_detection import non_max_suppression
+from skimage.transform import pyramid_gaussian
+
+
+def sliding_window(image, stepSize, windowSize):
+    for y in range(0, image.shape[0] - windowSize[1] + 1, stepSize):
+        for x in range(0, image.shape[1] - windowSize[0] + 1, stepSize):
+            yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])
+
+
+# HOG parameters (should match those used during training)
+hog_params = {
+    'orientations': 9,
+    'pixels_per_cell': (8, 8),
+    'cells_per_block': (2, 2),
+    'block_norm': 'L2-Hys'
+}
+
+# Load the pre-trained model (grid search object containing the best estimator)
+model = joblib.load('svm_natural_images_modelUTC.pkl')
+# If the saved object is a GridSearchCV, extract the best estimator.
+if hasattr(model, 'best_estimator_'):
+    model = model.best_estimator_
+
+# Detection parameters
+windowSize = (64, 64)
+hog_size = (64, 64)
+stepSize = 8  # Step size for sliding window.
+scale_factor = 1.01 # Scaling factor for image pyramid.
+detection_threshold = 0.85  # Minimum probability to consider a detection valid.
+
+# Load the test image (change the filename as needed)
+image = cv2.imread("test3.jpg")
+
+orig = image.copy()
+detections = []
+
+# Loop over the image pyramid
+for resized in tuple(pyramid_gaussian(image, downscale=scale_factor, channel_axis=-1)):
+    # The ratio of the current resized image relative to the original image
+    scale_ratio = image.shape[1] / float(resized.shape[1])
+
+    # Slide a window across the resized image
+    for (x, y, window) in sliding_window(resized, stepSize, windowSize):
+        # Ensure the window is of the desired size
+        if window.shape[0] != windowSize[1] or window.shape[1] != windowSize[0]:
+            continue
+
+        if window.dtype != np.uint8:
+            window = (window * 255).astype(np.uint8)
+
+
+        gray = cv2.cvtColor(window, cv2.COLOR_BGR2GRAY)
+        gray = cv2.resize(gray, hog_size, interpolation=cv2.INTER_AREA)
+
+        features = hog(gray, **hog_params)
+        features = features.reshape(1, -1)
+
+        # Predict the class and probability for this window
+        pred = model.predict(features)
+        prob = model.predict_proba(features)[0]
+
+        if 'person' in model.classes_:
+            person_index = list(model.classes_).index('person')
+            person_prob = prob[person_index]
+        else:
+            person_prob = 0
+
+        if pred[0] == 'person' and person_prob > detection_threshold:
+            startX = int(x * scale_ratio)
+            startY = int(y * scale_ratio)
+            endX = int((x + windowSize[0]) * scale_ratio)
+            endY = int((y + windowSize[1]) * scale_ratio)
+            detections.append((startX, startY, endX, endY))
+
+if len(detections) > 0:
+    detections = np.array(detections)
+    pick = non_max_suppression(detections, probs=None, overlapThresh=0.3)
+    # Draw the final bounding boxes on the original image.
+    for (xA, yA, xB, yB) in pick:
+        cv2.rectangle(orig, (xA, yA), (xB, yB), (0, 255, 0), 2)
+
+# Display the detections
+cv2.imshow("Detections", orig)
+cv2.waitKey(0)
+cv2.destroyAllWindows()