ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -13,14 +13,9 @@ img_height = 640
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class LetterBox:
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def __init__(self,
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new_shape=(img_width, img_height),
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auto=False,
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scaleFill=False,
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scaleup=True,
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center=True,
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stride=32):
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def __init__(
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self, new_shape=(img_width, img_height), auto=False, scaleFill=False, scaleup=True, center=True, stride=32
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):
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self.new_shape = new_shape
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self.auto = auto
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self.scaleFill = scaleFill
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@ -33,9 +28,9 @@ class LetterBox:
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if labels is None:
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labels = {}
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img = labels.get('img') if image is None else image
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img = labels.get("img") if image is None else image
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shape = img.shape[:2] # current shape [height, width]
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new_shape = labels.pop('rect_shape', self.new_shape)
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new_shape = labels.pop("rect_shape", self.new_shape)
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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@ -63,15 +58,16 @@ class LetterBox:
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)) if self.center else 0, int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)) if self.center else 0, int(round(dw + 0.1))
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT,
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value=(114, 114, 114)) # add border
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if labels.get('ratio_pad'):
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labels['ratio_pad'] = (labels['ratio_pad'], (left, top)) # for evaluation
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img = cv2.copyMakeBorder(
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img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)
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) # add border
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if labels.get("ratio_pad"):
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labels["ratio_pad"] = (labels["ratio_pad"], (left, top)) # for evaluation
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if len(labels):
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labels = self._update_labels(labels, ratio, dw, dh)
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labels['img'] = img
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labels['resized_shape'] = new_shape
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labels["img"] = img
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labels["resized_shape"] = new_shape
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return labels
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else:
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return img
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@ -79,15 +75,14 @@ class LetterBox:
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def _update_labels(self, labels, ratio, padw, padh):
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"""Update labels."""
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labels['instances'].convert_bbox(format='xyxy')
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labels['instances'].denormalize(*labels['img'].shape[:2][::-1])
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labels['instances'].scale(*ratio)
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labels['instances'].add_padding(padw, padh)
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labels["instances"].convert_bbox(format="xyxy")
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labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
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labels["instances"].scale(*ratio)
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labels["instances"].add_padding(padw, padh)
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return labels
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class Yolov8TFLite:
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def __init__(self, tflite_model, input_image, confidence_thres, iou_thres):
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"""
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Initializes an instance of the Yolov8TFLite class.
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@ -105,7 +100,7 @@ class Yolov8TFLite:
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self.iou_thres = iou_thres
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# Load the class names from the COCO dataset
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self.classes = yaml_load(check_yaml('coco128.yaml'))['names']
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self.classes = yaml_load(check_yaml("coco128.yaml"))["names"]
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# Generate a color palette for the classes
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self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3))
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@ -134,7 +129,7 @@ class Yolov8TFLite:
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cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2)
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# Create the label text with class name and score
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label = f'{self.classes[class_id]}: {score:.2f}'
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label = f"{self.classes[class_id]}: {score:.2f}"
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# Calculate the dimensions of the label text
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(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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@ -144,8 +139,13 @@ class Yolov8TFLite:
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label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
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# Draw a filled rectangle as the background for the label text
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cv2.rectangle(img, (int(label_x), int(label_y - label_height)),
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(int(label_x + label_width), int(label_y + label_height)), color, cv2.FILLED)
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cv2.rectangle(
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img,
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(int(label_x), int(label_y - label_height)),
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(int(label_x + label_width), int(label_y + label_height)),
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color,
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cv2.FILLED,
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)
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# Draw the label text on the image
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cv2.putText(img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
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@ -161,7 +161,7 @@ class Yolov8TFLite:
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# Read the input image using OpenCV
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self.img = cv2.imread(self.input_image)
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print('image befor', self.img)
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print("image before", self.img)
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# Get the height and width of the input image
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self.img_height, self.img_width = self.img.shape[:2]
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@ -209,8 +209,10 @@ class Yolov8TFLite:
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# Get the box, score, and class ID corresponding to the index
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box = boxes[i]
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gain = min(img_width / self.img_width, img_height / self.img_height)
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pad = round((img_width - self.img_width * gain) / 2 -
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0.1), round((img_height - self.img_height * gain) / 2 - 0.1)
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pad = (
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round((img_width - self.img_width * gain) / 2 - 0.1),
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round((img_height - self.img_height * gain) / 2 - 0.1),
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)
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box[0] = (box[0] - pad[0]) / gain
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box[1] = (box[1] - pad[1]) / gain
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box[2] = box[2] / gain
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@ -242,7 +244,7 @@ class Yolov8TFLite:
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output_details = interpreter.get_output_details()
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# Store the shape of the input for later use
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input_shape = input_details[0]['shape']
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input_shape = input_details[0]["shape"]
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self.input_width = input_shape[1]
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self.input_height = input_shape[2]
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@ -251,19 +253,19 @@ class Yolov8TFLite:
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img_data = img_data
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# img_data = img_data.cpu().numpy()
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# Set the input tensor to the interpreter
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print(input_details[0]['index'])
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print(input_details[0]["index"])
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print(img_data.shape)
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img_data = img_data.transpose((0, 2, 3, 1))
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scale, zero_point = input_details[0]['quantization']
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interpreter.set_tensor(input_details[0]['index'], img_data)
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scale, zero_point = input_details[0]["quantization"]
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interpreter.set_tensor(input_details[0]["index"], img_data)
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# Run inference
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interpreter.invoke()
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# Get the output tensor from the interpreter
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output = interpreter.get_tensor(output_details[0]['index'])
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scale, zero_point = output_details[0]['quantization']
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output = interpreter.get_tensor(output_details[0]["index"])
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scale, zero_point = output_details[0]["quantization"]
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output = (output.astype(np.float32) - zero_point) * scale
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output[:, [0, 2]] *= img_width
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@ -273,16 +275,15 @@ class Yolov8TFLite:
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return self.postprocess(self.img, output)
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if __name__ == '__main__':
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if __name__ == "__main__":
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# Create an argument parser to handle command-line arguments
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parser = argparse.ArgumentParser()
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parser.add_argument('--model',
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type=str,
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default='yolov8n_full_integer_quant.tflite',
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help='Input your TFLite model.')
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parser.add_argument('--img', type=str, default=str(ASSETS / 'bus.jpg'), help='Path to input image.')
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parser.add_argument('--conf-thres', type=float, default=0.5, help='Confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.5, help='NMS IoU threshold')
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parser.add_argument(
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"--model", type=str, default="yolov8n_full_integer_quant.tflite", help="Input your TFLite model."
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)
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parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image.")
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parser.add_argument("--conf-thres", type=float, default=0.5, help="Confidence threshold")
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parser.add_argument("--iou-thres", type=float, default=0.5, help="NMS IoU threshold")
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args = parser.parse_args()
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# Create an instance of the Yolov8TFLite class with the specified arguments
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@ -292,7 +293,7 @@ if __name__ == '__main__':
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output_image = detection.main()
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# Display the output image in a window
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cv2.imshow('Output', output_image)
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cv2.imshow("Output", output_image)
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# Wait for a key press to exit
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cv2.waitKey(0)
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