# Imports import cv2 import matplotlib.pyplot as plt import numpy as np from openvino.runtime import Core # Load the Model ie = Core() model = ie.read_model(model="model/horizontal-text-detection-0001.xml") compiled_model = ie.compile_model(model=model, device_name="CPU") input_layer_ir = compiled_model.input(0) output_layer_ir = compiled_model.output("boxes") # Load an Image # Text detection models expects image in BGR format image = cv2.imread("data/intel_rnb.jpg") # N,C,H,W = batch size, number of channels, height, width N, C, H, W = input_layer_ir.shape # Resize image to meet network expected input sizes resized_image = cv2.resize(image, (W, H)) # Reshape to network input shape input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0) plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)); # Do Inference # Create inference request boxes = compiled_model([input_image])[output_layer_ir] # Remove zero only boxes boxes = boxes[~np.all(boxes == 0, axis=1)] # Visualize Results # For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf] # Image passed here is in BGR format with changed width and height. To display it in colors expected by matplotlib we use cvtColor function def convert_result_to_image(bgr_image, resized_image, boxes, threshold=0.3, conf_labels=True): # Define colors for boxes and descriptions colors = {"red": (255, 0, 0), "green": (0, 255, 0)} # Fetch image shapes to calculate ratio (real_y, real_x), (resized_y, resized_x) = bgr_image.shape[:2], resized_image.shape[:2] ratio_x, ratio_y = real_x / resized_x, real_y / resized_y # Convert base image from bgr to rgb format rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB) # Iterate through non-zero boxes for box in boxes: # Pick confidence factor from last place in array conf = box[-1] if conf > threshold: # Convert float to int and multiply corner position of each box by x and y ratio # In case that bounding box is found at the top of the image, # we position upper box bar little lower to make it visible on image (x_min, y_min, x_max, y_max) = [ int(max(corner_position * ratio_y, 10)) if idx % 2 else int(corner_position * ratio_x) for idx, corner_position in enumerate(box[:-1]) ] # Draw box based on position, parameters in rectangle function are: image, start_point, end_point, color, thickness rgb_image = cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3) # Add text to image based on position and confidence # Parameters in text function are: image, text, bottom-left_corner_textfield, font, font_scale, color, thickness, line_type if conf_labels: rgb_image = cv2.putText( rgb_image, f"{conf:.2f}", (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, colors["red"], 1, cv2.LINE_AA, ) return rgb_image plt.figure(figsize=(10, 6)) plt.axis("off") plt.imshow(convert_result_to_image(image, resized_image, boxes, conf_labels=False));
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