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import cv2
import numpy as np
def prepare_input(image, input_shape):
input_height, input_width = input_shape
input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Resize input image
input_img = cv2.resize(input_img, (input_width, input_height))
# Scale input pixel values to 0 to 1
input_img = input_img / 255.0
input_img = input_img.transpose(2, 0, 1)
input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
return input_tensor
def process_output(output, ori_shape, input_shape, conf_threshold, iou_threshold, classes=[]):
predictions = output[0]
# predictions = np.squeeze(output[0])
# print(predictions.shape)
# print([p[5] for p in predictions])
# exit()
# Filter out object confidence scores below threshold
# obj_conf = predictions[:, 4]
obj_conf = predictions[:, 6]
# predictions = predictions[obj_conf > conf_threshold]
# obj_conf = obj_conf[obj_conf > conf_threshold]
# print(obj_conf[0])
# Multiply class confidence with bounding box confidence
# predictions[:, 5] *= obj_conf[:, np.newaxis]
# predictions[:, 6] *= obj_conf
# Get the scores
# scores = np.max(predictions[:, 5:], axis=1)
scores = predictions[:, 6]
# Filter out the objects with a low score
predictions = predictions[obj_conf > conf_threshold]
scores = scores[scores > conf_threshold]
if len(scores) == 0:
return [], [], []
# Get the class with the highest confidence
# class_ids = np.argmax(predictions[:, 5:], axis=1)
class_ids = predictions[:, 5].astype(np.uint16)
# Extract boxes from predictions
boxes = predictions[:, 1:5]
# Scale boxes to original image dimensions
boxes = rescale_boxes(boxes, ori_shape, input_shape)
# Convert boxes to xyxy format
# boxes = xywh2xyxy(boxes)
# Apply non-maxima suppression to suppress weak, overlapping bounding boxes
indices = nms(boxes, scores, iou_threshold)
dets = []
for i in indices:
if len(classes) > 0:
if class_ids[i] in classes:
dets.append([*boxes[i], scores[i], class_ids[i]])
else:
dets.append([*boxes[i], scores[i], class_ids[i]])
# return boxes[indices], scores[indices], class_ids[indices]
return np.array(dets)
def rescale_boxes(boxes, ori_shape, input_shape):
input_height, input_width = input_shape
img_height, img_width = ori_shape
# Rescale boxes to original image dimensions
input_shape = np.array([input_width, input_height, input_width, input_height])
boxes = np.divide(boxes, input_shape, dtype=np.float32)
boxes *= np.array([img_width, img_height, img_width, img_height])
return boxes
class_names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush']
# Create a list of colors for each class where each color is a tuple of 3 integer values
rng = np.random.default_rng(3)
colors = rng.uniform(0, 255, size=(len(class_names), 3))
def nms(boxes, scores, iou_threshold):
# Sort by score
sorted_indices = np.argsort(scores)[::-1]
keep_boxes = []
while sorted_indices.size > 0:
# Pick the last box
box_id = sorted_indices[0]
keep_boxes.append(box_id)
# Compute IoU of the picked box with the rest
ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
# Remove boxes with IoU over the threshold
keep_indices = np.where(ious < iou_threshold)[0]
# print(keep_indices.shape, sorted_indices.shape)
sorted_indices = sorted_indices[keep_indices + 1]
return keep_boxes
def compute_iou(box, boxes):
# Compute xmin, ymin, xmax, ymax for both boxes
xmin = np.maximum(box[0], boxes[:, 0])
ymin = np.maximum(box[1], boxes[:, 1])
xmax = np.minimum(box[2], boxes[:, 2])
ymax = np.minimum(box[3], boxes[:, 3])
# Compute intersection area
intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
# Compute union area
box_area = (box[2] - box[0]) * (box[3] - box[1])
boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
union_area = box_area + boxes_area - intersection_area
# Compute IoU
iou = intersection_area / union_area
return iou
def xywh2xyxy(x):
# Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
y = np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2
y[..., 1] = x[..., 1] - x[..., 3] / 2
y[..., 2] = x[..., 0] + x[..., 2] / 2
y[..., 3] = x[..., 1] + x[..., 3] / 2
return y
def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3):
mask_img = image.copy()
det_img = image.copy()
img_height, img_width = image.shape[:2]
size = min([img_height, img_width]) * 0.0006
text_thickness = int(min([img_height, img_width]) * 0.001)
# Draw bounding boxes and labels of detections
for box, score, class_id in zip(boxes, scores, class_ids):
color = colors[class_id]
x1, y1, x2, y2 = box.astype(int)
# Draw rectangle
cv2.rectangle(det_img, (x1, y1), (x2, y2), color, 2)
# Draw fill rectangle in mask image
cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)
label = class_names[class_id]
caption = f'{label} {int(score * 100)}%'
(tw, th), _ = cv2.getTextSize(text=caption, fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=size, thickness=text_thickness)
th = int(th * 1.2)
cv2.rectangle(det_img, (x1, y1),
(x1 + tw, y1 - th), color, -1)
cv2.rectangle(mask_img, (x1, y1),
(x1 + tw, y1 - th), color, -1)
cv2.putText(det_img, caption, (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX, size, (255, 255, 255), text_thickness, cv2.LINE_AA)
cv2.putText(mask_img, caption, (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX, size, (255, 255, 255), text_thickness, cv2.LINE_AA)
cv2.imwrite('reult.png', mask_img)
exit()
# def draw_detections(image, boxes, scores, class_ids, draw_scores=True, mask_alpha=0.4):
# return draw_detections(image, boxes, scores,
# class_ids, mask_alpha) |