def predict(request):
'''识别'''
request_data = json.loads(request.body)
# print('request_data:', request_data)
read = request_data['read']
img_data = request_data['img_data'].split(',')[1]
img_data = ImageHelper.base64ToBytes(img_data)
img_old = ImageHelper.bytesToOpencvImage(img_data)
# 图片大小
image_size = np.int32([416, 416])
# 缩放图片
img, _, padding = ImageHelper.opencvProportionalResize(img_old,
image_size,
bg_color=(0, 0, 0))
# print('imgType:', type(img))
width, height = ImageHelper.opencvGetImageSize(img_old)
image_size_old = np.int32([width, height])
print('imgSize:', width, height)
# 最后输出图片
predict_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 调整参数范围
predict_img = predict_img.astype(np.float32)
predict_img = predict_img / 255
# 增加一个维度
predict_img = np.expand_dims(predict_img, 0)
y_boxes, y_classes_id, y_scores, y_classes, y_confidence = model.Predict(
predict_img, confidence_thresh=0.5, scores_thresh=0.2, iou_thresh=0.5)
# 结果转物体框列表
y_boxes = y_boxes.numpy()
y_classes_id = y_classes_id.numpy()
y_scores = y_scores.numpy()
y_classes = y_classes.numpy()
y_confidence = y_confidence.numpy()
y_boxes[:, [0, 2]] = (y_boxes[:, [0, 2]] * image_size[0] - padding[2]) / (
image_size[0] - padding[2] - padding[3]) * image_size_old[0]
y_boxes[:, [1, 3]] = (y_boxes[:, [1, 3]] * image_size[1] - padding[0]) / (
image_size[1] - padding[0] - padding[1]) * image_size_old[1]
# 截取框超出图片部分
y_boxes[:, 0][y_boxes[:, 0] < 0] = 0
y_boxes[:, 1][y_boxes[:, 1] < 0] = 0
y_boxes[:, 2][y_boxes[:, 2] > image_size_old[0]] = image_size_old[0]
y_boxes[:, 3][y_boxes[:, 3] > image_size_old[1]] = image_size_old[1]
# 去掉无效框
y_mask = np.logical_and(y_boxes[:, 2] - y_boxes[:, 0] > 2,
y_boxes[:, 3] - y_boxes[:, 1] > 2)
y_boxes = y_boxes[y_mask]
y_classes_id = y_classes_id[y_mask]
y_scores = y_scores[y_mask]
y_classes = y_classes[y_mask]
y_confidence = y_confidence[y_mask]
y_boxes = y_boxes.astype(np.int32)
print('y_boxes:', y_boxes.shape)
print('y_classes_id:', y_classes_id.shape)
print('y_scores:', y_scores.shape)
print('y_classes:', y_classes.shape)
print('y_confidence:', y_confidence.shape)
# 画框
result_img = img_old.copy()
for i in range(y_boxes.shape[0]):
print('y_boxes:', y_boxes[i, :])
print('y_classes_id:', y_classes_id[i])
print('y_scores:', y_scores[i])
print('y_classes:', y_classes[i])
print('y_confidence:', y_confidence[i])
cv2.rectangle(result_img,
tuple(y_boxes[i, 0:2]),
tuple(y_boxes[i, 2:4]), (0, 0, 255),
thickness=1)
cv2.putText(result_img, classes_name[y_classes_id[i]],
tuple(y_boxes[i, 0:2]), cv2.FONT_HERSHEY_COMPLEX, 0.6,
(0, 100, 0), 1)
cv2.putText(result_img, str(y_scores[i]),
tuple(y_boxes[i, 0:2] + (0, 20)), cv2.FONT_HERSHEY_COMPLEX,
0.6, (0, 0, 100), 1)
jsonObj = {
'boxes':
y_boxes.tolist(),
'classes':
y_classes.tolist(),
'random_img':
ImageHelper.bytesTobase64(ImageHelper.opencvImageToBytes(img)),
'result_img':
ImageHelper.bytesTobase64(ImageHelper.opencvImageToBytes(result_img)),
}
# print('jsonObj:',jsonObj)
return HttpResponse(json.dumps(jsonObj), content_type="application/json")
def test():
'''训练'''
# 加载数据
image_wh = (416, 416)
# image_wh = (608, 608)
anchors = LoadAnchors(anchorsFile)
classes_name, classes_num = LoadClasses(classesFile)
# 构建模型
model = YoloV3Model(classes_num=classes_num,
anchors=anchors,
image_wh=image_wh)
# 编译模型
print('编译模型')
model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4))
# 加载模型
_ = model(tf.ones((1, image_wh[1], image_wh[0], 3)), training=False)
if os.path.exists(modelPath):
last_model_path = tf.train.latest_checkpoint(modelPath)
model.load_weights(last_model_path).expect_partial()
print('加载模型:{}'.format(last_model_path))
# model.summary()
img_old = ImageHelper.fileToOpencvImage(imageFile)
# 缩放图片
img, _, padding = ImageHelper.opencvProportionalResize(img_old,
image_wh,
bg_color=(0, 0, 0))
# print('imgType:', type(img))
width, height = ImageHelper.opencvGetImageSize(img_old)
image_size_old = np.int32([width, height])
print('imgSize:', width, height)
# 最后输出图片
predict_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 调整参数范围
predict_img = predict_img.astype(np.float32)
predict_img = predict_img / 255
# 增加一个维度
predict_img = np.expand_dims(predict_img, 0)
y_boxes, y_classes_id, y_scores, y_classes, y_confidence = model.Predict(
predict_img, confidence_thresh=0.5, scores_thresh=0.2, iou_thresh=0.5)
# 结果转物体框列表
y_boxes = y_boxes.numpy()
y_classes_id = y_classes_id.numpy()
y_scores = y_scores.numpy()
y_classes = y_classes.numpy()
y_confidence = y_confidence.numpy()
y_boxes[:, [0, 2]] = (y_boxes[:, [0, 2]] * image_wh[0] - padding[2]) / (
image_wh[0] - padding[2] - padding[3]) * image_size_old[0]
y_boxes[:, [1, 3]] = (y_boxes[:, [1, 3]] * image_wh[1] - padding[0]) / (
image_wh[1] - padding[0] - padding[1]) * image_size_old[1]
# 截取框超出图片部分
y_boxes[:, 0][y_boxes[:, 0] < 0] = 0
y_boxes[:, 1][y_boxes[:, 1] < 0] = 0
y_boxes[:, 2][y_boxes[:, 2] > image_size_old[0]] = image_size_old[0]
y_boxes[:, 3][y_boxes[:, 3] > image_size_old[1]] = image_size_old[1]
# 去掉无效框
y_mask = np.logical_and(y_boxes[:, 2] - y_boxes[:, 0] > 2,
y_boxes[:, 3] - y_boxes[:, 1] > 2)
y_boxes = y_boxes[y_mask]
y_classes_id = y_classes_id[y_mask]
y_scores = y_scores[y_mask]
y_classes = y_classes[y_mask]
y_confidence = y_confidence[y_mask]
y_boxes = y_boxes.astype(np.int32)
# print('y_boxes:', y_boxes.shape)
# print('y_classes_id:', y_classes_id.shape)
# print('y_scores:', y_scores.shape)
# print('y_classes:', y_classes.shape)
# print('y_confidence:', y_confidence.shape)
colors = []
for cr in range(0, 256, 32):
for cg in range(0, 256, 32):
for cb in range(0, 256, 32):
colors.append([cb, cg, cr])
colors = np.array(colors)
np.random.seed(11)
np.random.shuffle(colors)
np.random.seed(None)
result_img = img_old.copy()
for i in range(y_boxes.shape[0]):
# print('y_boxes:', y_boxes[i,:])
# print('y_classes_id:', y_classes_id[i])
# print('y_classes_name:', classes_name[y_classes_id[i]])
# print('y_scores:', y_scores[i])
# print('y_classes:', y_classes[i])
# print('y_confidence:', y_confidence[i])
cv2.rectangle(result_img,
tuple(y_boxes[i, 0:2]),
tuple(y_boxes[i, 2:4]),
colors[y_classes_id[i] % len(colors)].tolist(),
thickness=1)
cv2.putText(result_img, classes_name[y_classes_id[i]],
tuple(y_boxes[i, 0:2]), cv2.FONT_HERSHEY_COMPLEX, 0.6,
(0, 100, 0), 1)
cv2.putText(result_img, str(y_scores[i]),
tuple(y_boxes[i, 0:2] + (0, 20)), cv2.FONT_HERSHEY_COMPLEX,
0.6, (0, 0, 100), 1)
ImageHelper.showOpencvImage(result_img)
select_sub_path=True,
is_full_path=False)
with open(outFile, 'w', encoding='utf-8') as f:
for image_path in file_list:
# 图片路径
full_path = os.path.join(dataDir, image_path)
img_old = ImageHelper.fileToOpencvImage(full_path)
# 缩放图片
img, _, padding = ImageHelper.opencvProportionalResize(img_old,
image_wh,
bg_color=(0, 0,
0))
# print('imgType:', type(img))
width, height = ImageHelper.opencvGetImageSize(img_old)
image_size_old = np.int32([width, height])
# 最后输出图片
predict_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 调整参数范围
predict_img = predict_img.astype(np.float32)
predict_img = predict_img / 255
# 增加一个维度
predict_img = np.expand_dims(predict_img, 0)
y_boxes, y_classes_id, y_scores, y_classes, y_confidence = model.Predict(
predict_img,
confidence_thresh=0.5,
scores_thresh=0.2,
iou_thresh=0.5)
# 结果转物体框列表
y_boxes = y_boxes.numpy()