视觉/图像重磅干货，第一时间送达!
pip install torch torchvision torchaudio easyocrimport easyocrreader = easyocr.Reader(['en'])
reader = easyocr.Reader(['en'],gpu=False)reader = easyocr.Reader(['en'],model_storage_directory='path/to/directory')
[([[28, 22], [353, 22], [353, 72], [28, 72]], 'SERIAL NUMBER', 0.8874381662616708), ([[35, 75], [397, 75], [397, 137], [35, 137]], 'AOC1715821', 0.8521895819573561), ([[39, 255], [315, 255], [315, 299], [39, 299]], 'PART NUMBER', 0.9971079202290591), ([[42, 298], [370, 298], [370, 354], [42, 354]], '9-00864-01', 0.8142346378327698)]import easyocrreader = easyocr.Reader(['en'])result = reader.readtext('/path/of/image',detail=0)print(result)
['SERIAL NUMBER', 'AOC1715821', 'PART NUMBER', '9-00864-01']result = reader.readtext('/path/of/image',detail=0, paragraph=True)print(result)
['SERIAL NUMBER AOC1715821', 'PART NUMBER 9-00864-01']import easyocrreader = easyocr.Reader(['en'])result = reader.readtext('/path/of/image')for res in result:coord=res[0]text=res[1]conf=res[2]print(text)
SERIAL NUMBERAOC1715821PART NUMBER9-00864-01
pip install supervisionimport easyocrimport supervision as svimport cv2import numpy as npfrom google.colab.patches import cv2_imshow# Image pathImage_path = '/path/to/image'# Initialize EasyOCR reader (English language, CPU)reader = easyocr.Reader(['en'], gpu=False, model_storage_directory='/path/to/directory')# Perform text detection on the imageresult = reader.readtext(Image_path)# Load image using OpenCVimage = cv2.imread(Image_path)# Prepare lists for bounding boxes, confidences, class IDs, and labelsxyxy, confidences, class_ids, label = [], [], [], []# Extract data from OCR resultfor detection in result:bbox, text, confidence = detection[0], detection[1], detection[2]# Convert bounding box formatx_min = int(min([point[0] for point in bbox]))y_min = int(min([point[1] for point in bbox]))x_max = int(max([point[0] for point in bbox]))y_max = int(max([point[1] for point in bbox]))# Append data to listsxyxy.append([x_min, y_min, x_max, y_max])label.append(text)confidences.append(confidence)class_ids.append(0)# Convert to NumPy arraysdetections = sv.Detections(xyxy=np.array(xyxy),confidence=np.array(confidences),class_id=np.array(class_ids))# Annotate image with bounding boxes and labelsbox_annotator = sv.BoxAnnotator()label_annotator = sv.LabelAnnotator()annotated_image = box_annotator.annotate(scene=image, detections=detections)annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections, labels=label)# Display and save the annotated imagesv.plot_image(image=annotated_image)cv2.imwrite("Output.jpg", annotated_image)
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