405-paddle-ocr-webcam: PaddleOCR with OpenVINO


Sat Jun 18 2022 20:42:14 GMT+0000 (Coordinated Universal Time)

Saved by @OpenVINOtoolkit #python #openvino #openvino-notebooks #deeplearning #accelerated-inference #ocr #paddle-paddle #paddle-ocr #nlp

# Imports
import sys
import os
import cv2
import numpy as np
import paddle
import math
import time
import collections
from PIL import Image
from pathlib import Path
import tarfile
import urllib.request

from openvino.runtime import Core
from IPython import display
import copy

import notebook_utils as utils
import pre_post_processing as processing

# Models for PaddleOCR
# Define the function to download text detection and recognition models from PaddleOCR resources

def run_model_download(model_url, model_file_path):
    Download pre-trained models from PaddleOCR resources

        model_url: url link to pre-trained models
        model_file_path: file path to store the downloaded model
    model_name = model_url.split("/")[-1]
    if model_file_path.is_file(): 
        print("Model already exists")
        # Download the model from the server, and untar it.
        print("Downloading the pre-trained model... May take a while...")

        # create a directory
        os.makedirs("model", exist_ok=True)
        urllib.request.urlretrieve(model_url, f"model/{model_name} ")
        print("Model Downloaded")

        file = tarfile.open(f"model/{model_name} ")
        res = file.extractall("model")
        if not res:
            print(f"Model Extracted to {model_file_path}.")
            print("Error Extracting the model. Please check the network.")

# Download the Model for Text Detection
# Directory where model will be downloaded

det_model_url = "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar"
det_model_file_path = Path("model/ch_ppocr_mobile_v2.0_det_infer/inference.pdmodel")

run_model_download(det_model_url, det_model_file_path)

# Load the Model for Text Detection
# initialize inference engine for text detection
core = Core()
det_model = core.read_model(model=det_model_file_path)
det_compiled_model = core.compile_model(model=det_model, device_name="CPU")

# get input and output nodes for text detection
det_input_layer = det_compiled_model.input(0)
det_output_layer = det_compiled_model.output(0)

# Download the Model for Text Recognition
rec_model_url = "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar"
rec_model_file_path = Path("model/ch_ppocr_mobile_v2.0_rec_infer/inference.pdmodel")

run_model_download(rec_model_url, rec_model_file_path)

# Load the Model for Text Recognition with Dynamic Shape
# read the model and corresponding weights from file
rec_model = core.read_model(model=rec_model_file_path)

# assign dynamic shapes to every input layer on the last dimension
for input_layer in rec_model.inputs:
    input_shape = input_layer.partial_shape
    input_shape[3] = -1
    rec_model.reshape({input_layer: input_shape})

rec_compiled_model = core.compile_model(model=rec_model, device_name="CPU")

# get input and output nodes
rec_input_layer = rec_compiled_model.input(0)
rec_output_layer = rec_compiled_model.output(0)

# Preprocessing image functions for text detection and recognition
# Preprocess for text detection
def image_preprocess(input_image, size):
    Preprocess input image for text detection

        input_image: input image 
        size: value for the image to be resized for text detection model
    img = cv2.resize(input_image, (size, size))
    img = np.transpose(img, [2, 0, 1]) / 255
    img = np.expand_dims(img, 0)
    # NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
    img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
    img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
    img -= img_mean
    img /= img_std
    return img.astype(np.float32)

# Preprocess for text recognition
def resize_norm_img(img, max_wh_ratio):
    Resize input image for text recognition

        img: bounding box image from text detection 
        max_wh_ratio: value for the resizing for text recognition model
    rec_image_shape = [3, 32, 320]
    imgC, imgH, imgW = rec_image_shape
    assert imgC == img.shape[2]
    character_type = "ch"
    if character_type == "ch":
        imgW = int((32 * max_wh_ratio))
    h, w = img.shape[:2]
    ratio = w / float(h)
    if math.ceil(imgH * ratio) > imgW:
        resized_w = imgW
        resized_w = int(math.ceil(imgH * ratio))
    resized_image = cv2.resize(img, (resized_w, imgH))
    resized_image = resized_image.astype('float32')
    resized_image = resized_image.transpose((2, 0, 1)) / 255
    resized_image -= 0.5
    resized_image /= 0.5
    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
    padding_im[:, :, 0:resized_w] = resized_image
    return padding_im

def prep_for_rec(dt_boxes, frame):
    Preprocessing of the detected bounding boxes for text recognition

        dt_boxes: detected bounding boxes from text detection 
        frame: original input frame 
    ori_im = frame.copy()
    img_crop_list = [] 
    for bno in range(len(dt_boxes)):
        tmp_box = copy.deepcopy(dt_boxes[bno])
        img_crop = processing.get_rotate_crop_image(ori_im, tmp_box)
    img_num = len(img_crop_list)
    # Calculate the aspect ratio of all text bars
    width_list = []
    for img in img_crop_list:
        width_list.append(img.shape[1] / float(img.shape[0]))
    # Sorting can speed up the recognition process
    indices = np.argsort(np.array(width_list))
    return img_crop_list, img_num, indices

def batch_text_box(img_crop_list, img_num, indices, beg_img_no, batch_num):
    Batch for text recognition

        img_crop_list: processed detected bounding box images 
        img_num: number of bounding boxes from text detection
        indices: sorting for bounding boxes to speed up text recognition
        beg_img_no: the beginning number of bounding boxes for each batch of text recognition inference
        batch_num: number of images for each batch
    norm_img_batch = []
    max_wh_ratio = 0
    end_img_no = min(img_num, beg_img_no + batch_num)
    for ino in range(beg_img_no, end_img_no):
        h, w = img_crop_list[indices[ino]].shape[0:2]
        wh_ratio = w * 1.0 / h
        max_wh_ratio = max(max_wh_ratio, wh_ratio)
    for ino in range(beg_img_no, end_img_no):
        norm_img = resize_norm_img(img_crop_list[indices[ino]], max_wh_ratio)
        norm_img = norm_img[np.newaxis, :]

    norm_img_batch = np.concatenate(norm_img_batch)
    norm_img_batch = norm_img_batch.copy()
    return norm_img_batch

# Postprocessing image for text detection
def post_processing_detection(frame, det_results):
    Postprocess the results from text detection into bounding boxes

        frame: input image 
        det_results: inference results from text detection model
    ori_im = frame.copy()
    data = {'image': frame}
    data_resize = processing.DetResizeForTest(data)
    data_list = []
    keep_keys = ['image', 'shape']
    for key in keep_keys:
    img, shape_list = data_list

    shape_list = np.expand_dims(shape_list, axis=0) 
    pred = det_results[0]    
    if isinstance(pred, paddle.Tensor):
        pred = pred.numpy()
    segmentation = pred > 0.3

    boxes_batch = []
    for batch_index in range(pred.shape[0]):
        src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
        mask = segmentation[batch_index]
        boxes, scores = processing.boxes_from_bitmap(pred[batch_index], mask, src_w, src_h)
        boxes_batch.append({'points': boxes})
    post_result = boxes_batch
    dt_boxes = post_result[0]['points']
    dt_boxes = processing.filter_tag_det_res(dt_boxes, ori_im.shape)    
    return dt_boxes

# Main processing function for PaddleOCR
def run_paddle_ocr(source=0, flip=False, use_popup=False, skip_first_frames=0):
    Main function to run the paddleOCR inference:
    1. Create a video player to play with target fps (utils.VideoPlayer).
    2. Prepare a set of frames for text detection and recognition.
    3. Run AI inference for both text detection and recognition.
    4. Visualize the results.

        source: the webcam number to feed the video stream with primary webcam set to "0", or the video path.  
        flip: to be used by VideoPlayer function for flipping capture image
        use_popup: False for showing encoded frames over this notebook, True for creating a popup window.
        skip_first_frames: Number of frames to skip at the beginning of the video. 
    # create video player to play with target fps
    player = None
        player = utils.VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
        # Start video capturing
        if use_popup:
            title = "Press ESC to Exit"
            cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)

        processing_times = collections.deque()
        while True:
            # grab the frame
            frame = player.next()
            if frame is None:
                print("Source ended")
            # if frame larger than full HD, reduce size to improve the performance
            scale = 1280 / max(frame.shape)
            if scale < 1:
                frame = cv2.resize(src=frame, dsize=None, fx=scale, fy=scale,
            # preprocess image for text detection
            test_image = image_preprocess(frame, 640)
            # measure processing time for text detection
            start_time = time.time()
            # perform the inference step
            det_results = det_compiled_model([test_image])[det_output_layer]
            stop_time = time.time()

            # Postprocessing for Paddle Detection
            dt_boxes = post_processing_detection(frame, det_results)

            processing_times.append(stop_time - start_time)
            # use processing times from last 200 frames
            if len(processing_times) > 200:
            processing_time_det = np.mean(processing_times) * 1000

            # Preprocess detection results for recognition
            dt_boxes = processing.sorted_boxes(dt_boxes)  
            batch_num = 6
            img_crop_list, img_num, indices = prep_for_rec(dt_boxes, frame)
            # For storing recognition results, include two parts:
            # txts are the recognized text results, scores are the recognition confidence level 
            rec_res = [['', 0.0]] * img_num
            txts = [] 
            scores = []

            for beg_img_no in range(0, img_num, batch_num):

                # Recognition starts from here
                norm_img_batch = batch_text_box(
                    img_crop_list, img_num, indices, beg_img_no, batch_num)

                # Run inference for text recognition 
                rec_results = rec_compiled_model([norm_img_batch])[rec_output_layer]

                # Postprocessing recognition results
                postprocess_op = processing.build_post_process(processing.postprocess_params)
                rec_result = postprocess_op(rec_results)
                for rno in range(len(rec_result)):
                    rec_res[indices[beg_img_no + rno]] = rec_result[rno]   
                if rec_res:
                    txts = [rec_res[i][0] for i in range(len(rec_res))] 
                    scores = [rec_res[i][1] for i in range(len(rec_res))]
            image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            boxes = dt_boxes
            # draw text recognition results beside the image
            draw_img = processing.draw_ocr_box_txt(

            # Visualize PaddleOCR results
            f_height, f_width = draw_img.shape[:2]
            fps = 1000 / processing_time_det
            cv2.putText(img=draw_img, text=f"Inference time: {processing_time_det:.1f}ms ({fps:.1f} FPS)", 
                        org=(20, 40),fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=f_width / 1000,
                        color=(0, 0, 255), thickness=1, lineType=cv2.LINE_AA)
            # use this workaround if there is flickering
            if use_popup: 
                draw_img = cv2.cvtColor(draw_img, cv2.COLOR_RGB2BGR)
                cv2.imshow(winname=title, mat=draw_img)
                key = cv2.waitKey(1)
                # escape = 27
                if key == 27:
                # encode numpy array to jpg
                draw_img = cv2.cvtColor(draw_img, cv2.COLOR_RGB2BGR)
                _, encoded_img = cv2.imencode(ext=".jpg", img=draw_img,
                                              params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                # create IPython image
                i = display.Image(data=encoded_img)
                # display the image in this notebook
    # ctrl-c
    except KeyboardInterrupt:
    # any different error
    except RuntimeError as e:
        if player is not None:
            # stop capturing
        if use_popup:

# Run Live PaddleOCR with OpenVINO
run_paddle_ocr(source=0, flip=False, use_popup=False)

# Test OCR results on video file

video_file = "https://raw.githubusercontent.com/yoyowz/classification/master/images/test.mp4"
run_paddle_ocr(source=video_file, flip=False, use_popup=False, skip_first_frames=0)

This demo shows how to run PPOCR model on OpenVINO natively. Instead of exporting the PaddlePaddle model to ONNX and then convert to the Intermediate Representation (IR) format through OpenVINO Model Optimizer, we can now read directly from the PaddlePaddle Model without any conversions. PaddleOCR is an ultra-light OCR model trained with PaddlePaddle deep learning framework, that aims to create multilingual and practical OCR tools. The paddleOCR pre-trained model used in the demo refer to the "Chinese and English ultra-lightweight PP-OCR model (9.4M)". More open-sourced pre-trained models could be downloaded at PaddleOCR Github or PaddleOCR Gitee. If you have not yet installed OpenVINO™, please follow the Installation Guide to install all required dependencies. https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide