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YOLOv7.py
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YOLOv7.py
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import time
import cv2
import numpy as np
import onnxruntime
from yolov7.utils import xywh2xyxy, nms, draw_detections
class YOLOv7:
def __init__(self, path, conf_thres=0.7, iou_thres=0.5, official_nms=False):
self.conf_threshold = conf_thres
self.iou_threshold = iou_thres
self.official_nms = official_nms
# Initialize model
self.initialize_model(path)
def __call__(self, image):
return self.detect_objects(image)
def initialize_model(self, path):
self.session = onnxruntime.InferenceSession(path,
providers=['CUDAExecutionProvider',
'CPUExecutionProvider'])
# Get model info
self.get_input_details()
self.get_output_details()
self.has_postprocess = 'score' in self.output_names or self.official_nms
def detect_objects(self, image):
input_tensor = self.prepare_input(image)
# Perform inference on the image
outputs = self.inference(input_tensor)
if self.has_postprocess:
self.boxes, self.scores, self.class_ids = self.parse_processed_output(outputs)
else:
# Process output data
self.boxes, self.scores, self.class_ids = self.process_output(outputs)
return self.boxes, self.scores, self.class_ids
def prepare_input(self, image):
self.img_height, self.img_width = image.shape[:2]
input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Resize input image
input_img = cv2.resize(input_img, (self.input_width, self.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 inference(self, input_tensor):
start = time.perf_counter()
outputs = self.session.run(self.output_names, {self.input_names[0]: input_tensor})
print(f"Inference time: {(time.perf_counter() - start)*1000:.2f} ms")
return outputs
def process_output(self, output):
predictions = np.squeeze(output[0])
# Filter out object confidence scores below threshold
obj_conf = predictions[:, 4]
predictions = predictions[obj_conf > self.conf_threshold]
obj_conf = obj_conf[obj_conf > self.conf_threshold]
# Multiply class confidence with bounding box confidence
predictions[:, 5:] *= obj_conf[:, np.newaxis]
# Get the scores
scores = np.max(predictions[:, 5:], axis=1)
# Filter out the objects with a low score
predictions = predictions[scores > self.conf_threshold]
scores = scores[scores > self.conf_threshold]
if len(scores) == 0:
return [], [], []
# Get the class with the highest confidence
class_ids = np.argmax(predictions[:, 5:], axis=1)
# Get bounding boxes for each object
boxes = self.extract_boxes(predictions)
# Apply non-maxima suppression to suppress weak, overlapping bounding boxes
indices = nms(boxes, scores, self.iou_threshold)
return boxes[indices], scores[indices], class_ids[indices]
def parse_processed_output(self, outputs):
#Pinto's postprocessing is different from the official nms version
if self.official_nms:
scores = outputs[0][:,-1]
predictions = outputs[0][:, [0,5,1,2,3,4]]
else:
scores = np.squeeze(outputs[0], axis=1)
predictions = outputs[1]
# Filter out object scores below threshold
valid_scores = scores > self.conf_threshold
predictions = predictions[valid_scores, :]
scores = scores[valid_scores]
if len(scores) == 0:
return [], [], []
# Extract the boxes and class ids
# TODO: Separate based on batch number
batch_number = predictions[:, 0]
class_ids = predictions[:, 1].astype(int)
boxes = predictions[:, 2:]
# In postprocess, the x,y are the y,x
if not self.official_nms:
boxes = boxes[:, [1, 0, 3, 2]]
# Rescale boxes to original image dimensions
boxes = self.rescale_boxes(boxes)
return boxes, scores, class_ids
def extract_boxes(self, predictions):
# Extract boxes from predictions
boxes = predictions[:, :4]
# Scale boxes to original image dimensions
boxes = self.rescale_boxes(boxes)
# Convert boxes to xyxy format
boxes = xywh2xyxy(boxes)
return boxes
def rescale_boxes(self, boxes):
# Rescale boxes to original image dimensions
input_shape = np.array([self.input_width, self.input_height, self.input_width, self.input_height])
boxes = np.divide(boxes, input_shape, dtype=np.float32)
boxes *= np.array([self.img_width, self.img_height, self.img_width, self.img_height])
return boxes
def draw_detections(self, image, draw_scores=True, mask_alpha=0.4):
return draw_detections(image, self.boxes, self.scores,
self.class_ids, mask_alpha)
def get_input_details(self):
model_inputs = self.session.get_inputs()
self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
self.input_shape = model_inputs[0].shape
self.input_height = self.input_shape[2]
self.input_width = self.input_shape[3]
def get_output_details(self):
model_outputs = self.session.get_outputs()
self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]
if __name__ == '__main__':
from imread_from_url import imread_from_url
model_path = "../models/yolov7_736x1280.onnx"
# Initialize YOLOv7 object detector
yolov7_detector = YOLOv7(model_path, conf_thres=0.3, iou_thres=0.5)
img_url = "https://live.staticflickr.com/13/19041780_d6fd803de0_3k.jpg"
img = imread_from_url(img_url)
# Detect Objects
yolov7_detector(img)
# Draw detections
combined_img = yolov7_detector.draw_detections(img)
cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
cv2.imshow("Output", combined_img)
cv2.waitKey(0)