def regression_convert_to_box(out, th=0.17, class_num_case=6):
# interpolate the vector out from the neural network, generate the boxes
#boxes = yolo_net_out_to_car_boxes(out[0], threshold = 0.17)
#th = 0.17
#th = 0.17
boxes = yolo_net_out_to_car_boxes(out[0],
threshold=th,
class_num=class_num_case)
return boxes
def frame_func(image):
crop = image[300:650, 500:, :]
resized = cv2.resize(crop, (448, 448))
batch = np.array([resized[:, :, 0], resized[:, :, 1], resized[:, :, 2]])
batch = 2 * (batch / 255.) - 1
batch = np.expand_dims(batch, axis=0)
out = model.predict(batch)
boxes = yolo_net_out_to_car_boxes(out[0], threshold=0.17)
return draw_box(boxes, image, [[500, 1280], [300, 650]])
def frame_func(self, image):
crop = image
resized = cv2.resize(crop,(448,448))
batch = np.array([resized[:,:,0],resized[:,:,1],resized[:,:,2]])
batch = 2*(batch/255.) - 1
batch = np.expand_dims(batch, axis=0)
out = self.model.predict(batch)
boxes = yolo_net_out_to_car_boxes(out[0], threshold = 0.17)
return draw_box(boxes,image,[[0, image.shape[1]], [0, image.shape[0]]])
def predict(self,imagePath, threshold=0.17):
image = plt.imread(imagePath)
image_crop = image
resized = cv2.resize(image_crop,(448,448))
batch = np.transpose(resized,(2,0,1))
batch = 2*(batch/255.) - 1
temp = 2*(resized/255.) - 1
batch = np.expand_dims(batch, axis=0)
out = self.model.predict(batch)
boxes = yolo_net_out_to_car_boxes(out[0], threshold = 0.17)
return boxes
def inference_and_visualize_batch_images_car_detection(model):
# more examples
images = [plt.imread(file) for file in glob.glob('./test_images/*.jpg')]
batch = np.array([
np.transpose(cv2.resize(image[300:650, 500:, :], (448, 448)),
(2, 0, 1)) for image in images
])
batch = 2 * (batch / 255.) - 1
out = model.predict(batch)
f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3,
2,
figsize=(11, 10))
th = 0.17
for i, ax in zip(range(len(batch)), [ax1, ax2, ax3, ax4, ax5, ax6]):
#boxes = yolo_net_out_to_car_boxes(out[i], threshold = 0.17)
boxes = yolo_net_out_to_car_boxes(out[i], threshold=th)
print("boxes" + str(i))
print(boxes)
ax.imshow(draw_box(boxes, images[i], [[500, 1280], [300, 650]]))
pylab.show()
model.add(Dense(1470))
model.summary()
load_weights(model, 'weights\\yolo-tiny.weights')
imagePath = 'test_images\\test1.jpg'
image = plt.imread(imagePath, 'rb')
image_crop = image[300:650, 500:, :]
resized = cv2.resize(image_crop, (448, 448))
batch = np.transpose(resized, (2, 0, 1))
batch = 2 * (batch / 255.) - 1
batch = np.expand_dims(batch, axis=0)
#
img_width, img_height = 448, 448
img = load_img(imagePath, False, target_size=(img_width, img_height))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
out = model.predict(x)
print(np.argmax(out, axis=1))
#
boxes = yolo_net_out_to_car_boxes(out[0], threshold=0.17)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
ax1.imshow(image)
ax2.imshow(draw_box(boxes, plt.imread(imagePath), [[500, 1280], [300, 650]]))
# img = cv2.imread(imagePath)
# print(img.shape)
# cv2.imshow('Amanda', img)
# cv2.waitKey(0)
vehicle_model.add(Dense(256))
vehicle_model.add(Dense(4096))
vehicle_model.add(LeakyReLU(alpha=0.1))
vehicle_model.add(Dense(1470))
vehicle_model.summary()
load_weights(vehicle_model, './yolo-tiny.weights')
image = plt.imread(filename)
image_crop = image[300:650, 500:, :]
resized = cv2.resize(image_crop, (448, 448))
batch = np.transpose(resized, (2, 0, 1))
batch = 2 * (batch / 255.) - 1
batch = np.expand_dims(batch, axis=0)
out = vehicle_model.predict(batch)
boxes = yolo_net_out_to_car_boxes(out[0], threshold=0.12) #Parameter Tuning
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
ax1.imshow(image)
ax2.imshow(draw_box(boxes, plt.imread(filename), [[500, 1280], [300, 650]]))
plt.show()
def frame_function(image):
crop = image[300:650, 500:, :]
resized = cv2.resize(crop, (448, 448))
batch = np.array([resized[:, :, 0], resized[:, :, 1], resized[:, :, 2]])
batch = 2 * (batch / 255.) - 1
batch = np.expand_dims(batch, axis=0)
out = vehicle_model.predict(batch)
boxes = yolo_net_out_to_car_boxes(out[0],
threshold=0.12) #Parameter Tuning
def regression_convert_to_box(out):
# interpolate the vector out from the neural network, generate the boxes
#boxes = yolo_net_out_to_car_boxes(out[0], threshold = 0.17)
th = 0.17
boxes = yolo_net_out_to_car_boxes(out[0], threshold=th)
return boxes
