Create utils.py

utils.py

@@ -0,0 +1,66 @@
+import openvino as ov
+import cv2
+import numpy as np
+from pathlib import Path
+
+core = ov.Core()
+model = core.read_model(model='./model/horizontal-text-detection-0001.xml')
+compiled_model = core.compile_model(model = model, device_name="CPU")
+input_layer = compiled_model.input(0)
+output_layer = compiled_model.output("boxes")
+
+def preprocess(image, input_layer):
+    N, C, H, W = input_layer.shape
+    resized_image = cv2.resize(image, (W, H))
+    input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
+    return input_image, resized_image
+
+def predict_image(image, conf_threshold):
+    input_image, resized_image = preprocess(image, input_layer)
+    boxes = compiled_model([input_image])[output_layer]
+    boxes = boxes[~np.all(boxes == 0, axis=1)]
+    return boxes, resized_image
+
+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 the image shapes to calculate a 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 the 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 a confidence factor from the last place in an array.
+        conf = box[-1]
+        if conf > threshold:
+            # Convert float to int and multiply corner position of each box by x and y ratio.
+            # If the bounding box is found at the top of the image,
+            # position the upper box bar little lower to make it visible on the 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 a box based on the 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 the 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