操作步骤
参考快速开始
环境准备
为了更快的体验产品，这里选择了使用ModelScope提供的远程环境，即Notebook进行开发，更加便捷。
- “快速开始”文档中，并未给出进入notebook的链接，需要从个人中心进入，https://modelscope.cn/#/my/mynotebook
模型调试：文本检测
参考：https://www.modelscope.cn/models/damo/cv_resnet18_ocr-detection-line-level_damo/summary
- 准备图像文件。
- 将图像文件上传至notebook（可拖拽），或使用url。
- 输入下列代码。
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import cv2
ocr_detection = pipeline(Tasks.ocr_detection, model='damo/cv_resnet18_ocr-detection-line-level_damo')
# ocr_detection = pipeline(Tasks.ocr_detection, model='damo/cv_resnet18_ocr-detection-word-level_damo')
# read file
img_path = 'ocr_detection.jpg'
img = cv2.imread(img_path)
result = ocr_detection(img)
print(result)
# or read url
img_url = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/ocr_detection.jpg'
result_url = ocr_detection(img_url)
print(result_url)
上面展示的是文本行检测模型的使用方法。
如需使用单词检测模型，请替换为第6行注释的模型，并参考https://www.modelscope.cn/models/damo/cv_resnet18_ocr-detection-word-level_damo/summary。
模型调试：文本识别
参考：https://www.modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-general_damo/summary
- 准备图像文件。
- 将图像文件上传至notebook（可拖拽），或使用url。
- 输入下列代码。
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import cv2
ocr_recognition = pipeline(Tasks.ocr_recognition, model='damo/cv_convnextTiny_ocr-recognition-general_damo')
# ocr_recognition = pipeline(Tasks.ocr_recognition, model='damo/cv_convnextTiny_ocr-recognition-scene_damo')
# ocr_recognition = pipeline(Tasks.ocr_recognition, model='damo/cv_convnextTiny_ocr-recognition-document_damo')
# ocr_recognition = pipeline(Tasks.ocr_recognition, model='damo/cv_convnextTiny_ocr-recognition-handwritten_damo')
# read file
img_path = 'ocr_recognition.jpg'
img = cv2.imread(img_path)
result = ocr_recognition(img)
print(result)
# or read url
img_url = 'http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/mass_img_tmp_20220922/ocr_recognition.jpg'
result_url = ocr_recognition(img_url)
print(result_url)
上面展示的是通用文本识别模型的使用方法。
如需使用自然场景文本识别模型，请替换为第6行注释的模型，并参考https://www.modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-scene_damo/summary。
如需使用印刷文档文本识别模型，请替换为第7行注释的模型，并参考https://www.modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-document_damo/summary。
如需使用手写文本识别模型，请替换为第8行注释的模型，并参考https://www.modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-handwritten_damo/summary。
模型调试：检测识别串联
有了上述的基础，我们串联文本检测和文本识别模型，以实现完整的OCR功能，输入下列代码。
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import numpy as np
import cv2
import math
# scripts for crop images
def crop_image(img, position):
def distance(x1,y1,x2,y2):
return math.sqrt(pow(x1 - x2, 2) + pow(y1 - y2, 2))
position = position.tolist()
for i in range(4):
for j in range(i+1, 4):
if(position[i][0] > position[j][0]):
tmp = position[j]
position[j] = position[i]
position[i] = tmp
if position[0][1] > position[1][1]:
tmp = position[0]
position[0] = position[1]
position[1] = tmp
if position[2][1] > position[3][1]:
tmp = position[2]
position[2] = position[3]
position[3] = tmp
x1, y1 = position[0][0], position[0][1]
x2, y2 = position[2][0], position[2][1]
x3, y3 = position[3][0], position[3][1]
x4, y4 = position[1][0], position[1][1]
corners = np.zeros((4,2), np.float32)
corners[0] = [x1, y1]
corners[1] = [x2, y2]
corners[2] = [x4, y4]
corners[3] = [x3, y3]
img_width = distance((x1+x4)/2, (y1+y4)/2, (x2+x3)/2, (y2+y3)/2)
img_height = distance((x1+x2)/2, (y1+y2)/2, (x4+x3)/2, (y4+y3)/2)
corners_trans = np.zeros((4,2), np.float32)
corners_trans[0] = [0, 0]
corners_trans[1] = [img_width - 1, 0]
corners_trans[2] = [0, img_height - 1]
corners_trans[3] = [img_width - 1, img_height - 1]
transform = cv2.getPerspectiveTransform(corners, corners_trans)
dst = cv2.warpPerspective(img, transform, (int(img_width), int(img_height)))
return dst
def order_point(coor):
arr = np.array(coor).reshape([4, 2])
sum_ = np.sum(arr, 0)
centroid = sum_ / arr.shape[0]
theta = np.arctan2(arr[:, 1] - centroid[1], arr[:, 0] - centroid[0])
sort_points = arr[np.argsort(theta)]
sort_points = sort_points.reshape([4, -1])
if sort_points[0][0] > centroid[0]:
sort_points = np.concatenate([sort_points[3:], sort_points[:3]])
sort_points = sort_points.reshape([4, 2]).astype('float32')
return sort_points
ocr_detection = pipeline(Tasks.ocr_detection, model='damo/cv_resnet18_ocr-detection-line-level_damo')
ocr_recognition = pipeline(Tasks.ocr_recognition, model='damo/cv_convnextTiny_ocr-recognition-general_damo')
img_path = 'ocr_detection.jpg'
image_full = cv2.imread(img_path)
det_result = ocr_detection(image_full)
det_result = det_result['polygons']
for i in range(det_result.shape[0]):
pts = order_point(det_result[i])
image_crop = crop_image(image_full, pts)
result = ocr_recognition(image_crop)
print("box: %s" % ','.join([str(e) for e in list(pts.reshape(-1))]))
print("text: %s" % result['text'])
至此，我们已经在代码层面完成了两个模型的调试和串联，并实现了完整的OCR功能。
