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50 lines
2.0 KiB
Python
50 lines
2.0 KiB
Python
import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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# Function to read labels from text files.
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def ReadLabelFile(file_path):
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with open(file_path, 'r') as f:
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lines = f.readlines()
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ret = {}
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for line in lines:
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pair = line.strip().split(maxsplit=1)
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ret[int(pair[0])] = pair[1].strip()
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return ret
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# convert shared memory array into numpy array
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def tonumpyarray(mp_arr):
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return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
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def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, score, area):
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color = COLOR_MAP[label]
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display_text = "{}: {}% {}".format(label,int(score*100),int(area))
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cv2.rectangle(frame, (x_min, y_min),
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(x_max, y_max),
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color, 2)
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font_scale = 0.5
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font = cv2.FONT_HERSHEY_SIMPLEX
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# get the width and height of the text box
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size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2)
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text_width = size[0][0]
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text_height = size[0][1]
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line_height = text_height + size[1]
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# set the text start position
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text_offset_x = x_min
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text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
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# make the coords of the box with a small padding of two pixels
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textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
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cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
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cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
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# Path to frozen detection graph. This is the actual model that is used for the object detection.
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PATH_TO_CKPT = '/frozen_inference_graph.pb'
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# List of the strings that is used to add correct label for each box.
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PATH_TO_LABELS = '/label_map.pbtext'
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LABELS = ReadLabelFile(PATH_TO_LABELS)
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cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
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COLOR_MAP = {}
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for key, val in LABELS.items():
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COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3]) |