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ocr_test.py
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from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import os
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
def process_image_to_text(img_path, output_txt='output.txt', ocr_detection=None, ocr_recognition=None):
try:
# Read the image from the path
image_full = cv2.imread(img_path)
# Check if image is loaded properly
if image_full is None:
raise ValueError("Image could not be read.")
# Perform OCR detection
det_result = ocr_detection(image_full)
det_result = det_result['polygons']
# Open a file to write the OCR results
with open(output_txt, 'w', encoding='utf-8') as file:
for i in range(det_result.shape[0]):
# Order points for cropping
pts = order_point(det_result[i])
# Crop the image based on detected points
image_crop = crop_image(image_full, pts)
# Perform OCR recognition on the cropped image
result = ocr_recognition(image_crop)
text = result['text']
# Write the detected text to the file
if isinstance(text, list): # Check if the text is a list
text = ''.join(text) # Join all items in the list into a single string
# Write the processed text to the file
file.write(text + ' ')
print(f"OCR process completed and results are saved to '{output_txt}'.")
except Exception as e:
# In case of any error during the process, write an empty file
print(f"An error occurred: {str(e)}")
with open(output_txt, 'w') as file:
pass # Creating an empty file
def save_first_frame(video_path, output_image_path):
# 打开视频文件
cap = cv2.VideoCapture(video_path)
# 检查视频是否成功打开
if not cap.isOpened():
print("Error: Could not open video.")
return None
# 读取第一帧
ret, frame = cap.read()
cap.release()
if ret:
cv2.imwrite(output_image_path, frame)
print(f"First frame saved to {output_image_path}")
return output_image_path
else:
print("Error: Could not read the first frame.")
return None
def process_video_directory(video_dir, OCR_output_directory, keyframe_output_directory):
# Initialize the OCR models
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')
# 有两层文件夹，第一层文件夹为视频文件夹，第二层文件夹为视频文件，使用两个循环分别处理
for video_folder in os.listdir(video_dir):
video_folder_path = os.path.join(video_dir, video_folder)
if os.path.isdir(video_folder_path):
for video_file in os.listdir(video_folder_path):
video_file_path = os.path.join(video_folder_path, video_file)
video_name = os.path.splitext(os.path.basename(video_file_path))[0]
output_image_path = os.path.join(keyframe_output_directory,video_folder, f"{video_name}_keyframe.jpg")
output_txt_path = os.path.join(OCR_output_directory, video_folder, f"{video_name}.txt")
if not os.path.exists(output_image_path):
# 新建文件夹
if not os.path.exists(os.path.join(keyframe_output_directory,video_folder)):
os.makedirs(os.path.join(keyframe_output_directory,video_folder))
if not os.path.exists(os.path.join(OCR_output_directory,video_folder)):
os.makedirs(os.path.join(OCR_output_directory,video_folder))
img_path, output_txt_path = process_single_video(video_file_path, output_image_path=output_image_path, output_txt_path = output_txt_path, ocr_detection=ocr_detection, ocr_recognition=ocr_recognition)
print(f"Processed video {video_name}.")
else:
print(f"Key frame {output_image_path} already exists, skipping.")
def process_single_video(video_path, output_image_path, output_txt_path, ocr_detection, ocr_recognition):
# Save the first frame of the video
img_path = save_first_frame(video_path, output_image_path)
if img_path is None:
raise Exception("Failed to extract key frame.")
# Process the image to extract text
process_image_to_text(img_path, output_txt_path, ocr_detection=ocr_detection, ocr_recognition=ocr_recognition)
return img_path, output_txt_path
if __name__ == '__main__':
video_directory = '/root/bishe/end_output/虚假宣传/segment_video_0.8' # Specify your video directory
OCR_output_directory = '/root/bishe/end_output/虚假宣传/OCR'
keyframe_output_directory = '/root/bishe/end_output/虚假宣传/keyframe' # Specify your output directory
process_video_directory(video_directory, OCR_output_directory, keyframe_output_directory)