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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
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