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| # common_detection.py | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| def perform_detection(image, interpreter, labels, input_details, output_details, height, width, floating_model): | |
| imH, imW, _ = image.shape | |
| image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| image_resized = cv2.resize(image_rgb, (width, height)) | |
| input_data = np.expand_dims(image_resized, axis=0) | |
| input_mean = 127.5 | |
| input_std = 127.5 | |
| if floating_model: | |
| input_data = (np.float32(input_data) - input_mean) / input_std | |
| interpreter.set_tensor(input_details[0]['index'], input_data) | |
| interpreter.invoke() | |
| boxes = interpreter.get_tensor(output_details[0]['index'])[0] | |
| classes = interpreter.get_tensor(output_details[1]['index'])[0] | |
| scores = interpreter.get_tensor(output_details[2]['index'])[0] | |
| detections = [] | |
| for i in range(len(scores)): | |
| if (scores[i] > 0.5): | |
| ymin = int(max(1, (boxes[i][0] * imH))) | |
| xmin = int(max(1, (boxes[i][1] * imW))) | |
| ymax = int(min(imH, (boxes[i][2] * imH))) | |
| xmax = int(min(imW, (boxes[i][3] * imW))) | |
| cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (10, 255, 0), 2) | |
| object_name = labels[int(classes[i])] | |
| label = f'{object_name}: {int(scores[i] * 100)}%' | |
| labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) | |
| label_ymin = max(ymin, labelSize[1] + 10) | |
| cv2.rectangle(image, (xmin, label_ymin - labelSize[1] - 10), (xmin + labelSize[0], label_ymin + baseLine - 10), (255, 255, 255), cv2.FILLED) | |
| cv2.putText(image, label, (xmin, label_ymin - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) | |
| detections.append([object_name, scores[i], xmin, ymin, xmax, ymax]) | |
| return image | |