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| import cv2 | |
| import numpy as np | |
| def calculate_deforestation_metrics(mask, threshold=0.5): | |
| """ | |
| Calculate deforestation metrics from the predicted mask | |
| Args: | |
| mask: Predicted mask (numpy array) | |
| threshold: Threshold to binarize the mask (default is 0.5) | |
| Returns: | |
| Dictionary containing deforestation metrics | |
| """ | |
| # Binarize the mask | |
| binary_mask = (mask > threshold).astype(np.uint8) | |
| # Calculate pixel counts | |
| total_pixels = binary_mask.size | |
| forest_pixels = np.sum(binary_mask) | |
| deforested_pixels = total_pixels - forest_pixels | |
| # Calculate percentages | |
| forest_percentage = (forest_pixels / total_pixels) * 100 | |
| deforested_percentage = (deforested_pixels / total_pixels) * 100 | |
| # Determine deforestation level | |
| if deforested_percentage < 20: | |
| level = "Low" | |
| elif deforested_percentage < 50: | |
| level = "Medium" | |
| else: | |
| level = "High" | |
| return { | |
| "forest_pixels": forest_pixels, | |
| "deforested_pixels": deforested_pixels, | |
| "forest_percentage": forest_percentage, | |
| "deforested_percentage": deforested_percentage, | |
| "deforestation_level": level, | |
| } | |
| def create_overlay(original_image, mask, threshold=0.5, alpha=0.5): | |
| """ | |
| Create a visualization by overlaying the mask on the original image | |
| Args: | |
| original_image: Original RGB image | |
| mask: Predicted mask | |
| threshold: Threshold to binarize the mask | |
| alpha: Opacity of the overlay | |
| Returns: | |
| Overlay image | |
| """ | |
| # Resize mask to match original image if needed | |
| if original_image.shape[:2] != mask.shape[:2]: | |
| mask = cv2.resize(mask, (original_image.shape[1], original_image.shape[0])) | |
| # Create binary mask | |
| binary_mask = (mask > threshold).astype(np.uint8) * 255 | |
| # Create a colored mask (green for forest, red for deforested) | |
| colored_mask = np.zeros_like(original_image) | |
| colored_mask[binary_mask == 255] = [0, 255, 0] # Green for forest | |
| colored_mask[binary_mask == 0] = [150, 75, 0] # Brown for deforested | |
| # Create overlay | |
| overlay = cv2.addWeighted(original_image, 1 - alpha, colored_mask, alpha, 0) | |
| return overlay | |
| CLASS_NAMES = ['bike-bicycle', 'bus-truck', 'car', 'fire', 'human', 'smoke'] | |
| COLORS = np.random.uniform(0, 255, size=(len(CLASS_NAMES), 3)) | |
| def preprocess(image, img_size=640): | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| image = cv2.resize(image, (img_size, img_size)) | |
| image = image.transpose((2, 0, 1)) # HWC to CHW | |
| image = np.ascontiguousarray(image, dtype=np.float32) / 255.0 | |
| return image[np.newaxis, ...] | |
| def postprocess(outputs, conf_thresh=0.5, iou_thresh=0.5): | |
| outputs = outputs[0].transpose() | |
| boxes, scores, class_ids = [], [], [] | |
| for row in outputs: | |
| cls_scores = row[4:4+len(CLASS_NAMES)] | |
| class_id = np.argmax(cls_scores) | |
| max_score = cls_scores[class_id] | |
| if max_score >= conf_thresh: | |
| cx, cy, w, h = row[:4] | |
| x = (cx - w/2).item() # Convert to Python float | |
| y = (cy - h/2).item() | |
| width = w.item() | |
| height = h.item() | |
| boxes.append([x, y, width, height]) | |
| scores.append(float(max_score)) | |
| class_ids.append(int(class_id)) | |
| if len(boxes) > 0: | |
| # Convert to list of lists with native Python floats | |
| boxes = [[float(x) for x in box] for box in boxes] | |
| scores = [float(score) for score in scores] | |
| indices = cv2.dnn.NMSBoxes( | |
| bboxes=boxes, | |
| scores=scores, | |
| score_threshold=conf_thresh, | |
| nms_threshold=iou_thresh | |
| ) | |
| if len(indices) > 0: | |
| boxes = [boxes[i] for i in indices.flatten()] | |
| scores = [scores[i] for i in indices.flatten()] | |
| class_ids = [class_ids[i] for i in indices.flatten()] | |
| return boxes, scores, class_ids | |