heshuju
commented
3 years ago I use tiny weight to predict image, but it can not detect.So I want to check accuracy.The loss is < 0.1，but " mask = box_scores >= score_threshold"'s result is all False.
Here is my predict code from keras.layers import Input from keras import backend as K from yolo3.utils import letterbox_image from PIL import Image, ImageFont, ImageDraw import numpy as np import cv2 import os from yolo import YOLO as YOLO def get_layer_output(model, x, index = -1): layer = K.function([model.input], [model.layers[index].output]) return layer([x])[0] K.set_learning_phase(0) num_anchors = 6 num_classes = 1 model_image_size = (416, 416) model_path = "trained_weights_final.h5" frame = cv2.imread("person.jpg")
print(type(image))
image = Image.fromarray(frame) image = letterbox_image(image, model_image_size) image = np.array(image, dtype='float32') print(image.shape) image /= 255. image = np.expand_dims(image, 0) # Add batch dimension.
image = numpy.array(image)
yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) yolo_model.load_weights(model_path) # make sure model, anchors and classes match
predicted = (get_layer_output(yolo_model, image, -1), get_layer_output(yolo_model, image, -2))
anchors_path = YOLO.get_defaults('anchors_path') anchors_path = os.path.expanduser(anchors_path) print(anchors_path) anchors = [] with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] anchors = np.array(anchors).reshape(-1, 2) out1, out2 = yolo_model.predict(image) print(np.array(out1).shape, np.array(out2).shape) predicted = [out1, out2] boxes, scores, classes = yolo_eval(predicted, anchors, num_classes, model_image_size)
I use tiny weight to predict image, but it can not detect.So I want to check accuracy.The loss is < 0.1，but " mask = box_scores >= score_threshold"'s result is all False. Here is my predict code
from keras.layers import Input from keras import backend as K from yolo3.utils import letterbox_image from PIL import Image, ImageFont, ImageDraw import numpy as np import cv2 import os from yolo import YOLO as YOLO def get_layer_output(model, x, index = -1): layer = K.function([model.input], [model.layers[index].output]) return layer([x])[0] K.set_learning_phase(0) num_anchors = 6 num_classes = 1 model_image_size = (416, 416) model_path = "trained_weights_final.h5" frame = cv2.imread("person.jpg")
print(type(image))
image = Image.fromarray(frame) image = letterbox_image(image, model_image_size) image = np.array(image, dtype='float32') print(image.shape) image /= 255. image = np.expand_dims(image, 0) # Add batch dimension.
image = numpy.array(image)
yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) yolo_model.load_weights(model_path) # make sure model, anchors and classes match
predicted = (get_layer_output(yolo_model, image, -1), get_layer_output(yolo_model, image, -2))
anchors_path = YOLO.get_defaults('anchors_path') anchors_path = os.path.expanduser(anchors_path) print(anchors_path) anchors = [] with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] anchors = np.array(anchors).reshape(-1, 2) out1, out2 = yolo_model.predict(image) print(np.array(out1).shape, np.array(out2).shape) predicted = [out1, out2] boxes, scores, classes = yolo_eval(predicted, anchors, num_classes, model_image_size)