476 lines
19 KiB
Python
476 lines
19 KiB
Python
import cv2
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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import unicodedata
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from loguru import logger
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FONT_PATH = "./DrGraph/appIOs/conf/platech.ttf"
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zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
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def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False):
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if unify:
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x, y, width, height = font.getbbox("标") # 统一数组大小
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else:
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x, y, width, height = font.getbbox(label)
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text_image = np.zeros((height, width, 3), dtype=np.uint8)
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text_image = Image.fromarray(text_image)
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draw = ImageDraw.Draw(text_image)
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draw.rectangle((0, 0, width, height), fill=tuple(color))
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draw.text((0, -1), label, fill=(255, 255, 255), font=font)
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im_array = np.asarray(text_image)
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# scale = fontSize / height
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# im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
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scale = height / fontSize
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im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
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return im_array
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def get_label_arrays(labelNames, colors, fontSize=40, fontPath="platech.ttf"):
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font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
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label_arraylist = [get_label_array(colors[i % 20], label_name, font, fontSize) for i, label_name in
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enumerate(labelNames)]
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return label_arraylist
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def get_label_array_dict(colors, fontSize=40, fontPath="platech.ttf"):
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font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
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all_chinese_characters = []
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for char in range(0x4E00, 0x9FFF + 1): # 中文
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chinese_character = chr(char)
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if unicodedata.category(chinese_character) == 'Lo':
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all_chinese_characters.append(chinese_character)
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for char in range(0x0041, 0x005B): # 大写字母
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all_chinese_characters.append(chr(char))
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for char in range(0x0061, 0x007B): # 小写字母
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all_chinese_characters.append(chr(char))
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for char in range(0x0030, 0x003A): # 数字
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all_chinese_characters.append(chr(char))
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zh_dict = {}
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for code in all_chinese_characters:
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arr = get_label_array(colors[2], code, font, fontSize, unify=True)
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zh_dict[code] = arr
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return zh_dict
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def get_label_left(x0,y1,label_array,img):
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imh, imw = img.shape[0:2]
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lh, lw = label_array.shape[0:2]
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# x1 框框左上x位置 + 描述的宽
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# y0 框框左上y位置 - 描述的高
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x1, y0 = x0 + lw, y1 - lh
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# 如果y0小于0, 说明超过上边框
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if y0 < 0:
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y0 = 0
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# y1等于文字高度
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y1 = y0 + lh
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# 如果y1框框的高大于图片高度
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if y1 > imh:
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# y1等于图片高度
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y1 = imh
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# y0等于y1减去文字高度
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y0 = y1 - lh
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# 如果x0小于0
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if x0 < 0:
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x0 = 0
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x1 = x0 + lw
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if x1 > imw:
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x1 = imw
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x0 = x1 - lw
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return x0,y0,x1,y1
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def get_label_right(x1,y0,label_array):
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lh, lw = label_array.shape[0:2]
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# x1 框框右上x位置 + 描述的宽
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# y0 框框右上y位置 - 描述的高
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x0, y1 = x1 - lw, y0 - lh
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# 如果y0小于0, 说明超过上边框
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if y0 < 0 or y1 < 0:
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y1 = 0
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# y1等于文字高度
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y0 = y1 + lh
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# 如果x0小于0
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if x0 < 0 or x1 < 0:
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x0 = 0
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x1 = x0 + lw
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return x0,y1,x1,y0
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def xywh2xyxy(box):
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if not isinstance(box[0], (list, tuple, np.ndarray)):
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xc, yc, w, h = int(box[0]), int(box[1]), int(box[2]), int(box[3])
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bw, bh = int(w / 2), int(h / 2)
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lt, yt, rt, yr = xc - bw, yc - bh, xc + bw, yc + bh
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box = [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
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return box
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def xywh2xyxy2(param):
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if not isinstance(param[0], (list, tuple, np.ndarray)):
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xc, yc, x2, y2 = int(param[0]), int(param[1]), int(param[2]), int(param[3])
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return [(xc, yc), (x2, yc), (x2, y2), (xc, y2)], float(param[4]), int(param[5])
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# bw, bh = int(w / 2), int(h / 2)
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# lt, yt, rt, yr = xc - bw, yc - bh, xc + bw, yc + bh
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# return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
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return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2])
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def xy2xyxy(box):
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if not isinstance(box[0], (list, tuple, np.ndarray)):
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x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
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# 顺时针
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box = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
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return box
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def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False, border=None):
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# 识别问题描述图片的高、宽
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# 图片的长度和宽度
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if border is not None:
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border = np.array(border,np.int32)
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color,label_array=draw_name_border(box,color,label_array,border)
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#img = draw_transparent_red_polygon(img,border,'',alpha=0.1)
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lh, lw = label_array.shape[0:2]
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tl = config[0]
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if isinstance(box[-1], np.ndarray):
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return draw_name_points(img,box,color)
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label = ' %.2f' % score
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box = xywh2xyxy(box)
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# 框框左上的位置
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x0, y1 = box[0][0], box[0][1]
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x0, y0, x1, y1 = get_label_left(x0, y1, label_array, img)
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# box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
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'''
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1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
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2. box（array）：为所画多边形的顶点坐标
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3. 所画四边形是否闭合，通常为True
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4. color（tuple）：BGR三个通道的值
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5. thickness（int）：画线的粗细
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6. shift：顶点坐标中小数的位数
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'''
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img[y0:y1, x0:x1, :] = label_array
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box1 = np.asarray(box, np.int32)
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cv2.polylines(img, [box1], True, color, tl)
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pts_cls = [(x0, y0), (x1, y1)]
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# 把英文字符score画到类别旁边
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# tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
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# tf = max(tl, 1)
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# fontScale = float(format(imw / 1920 * 1.1, '.2f')) or tl * 0.33
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# fontScale = tl * 0.33
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'''
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1. text：要计算大小的文本内容，类型为字符串。
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2. fontFace：字体类型，例如cv2.FONT_HERSHEY_SIMPLEX等。
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3. fontScale：字体大小的缩放因子，例如1.2表示字体大小增加20%。
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4. thickness：文本线条的粗细，以像素为单位。
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5. (text_width, text_height)：给定文本在指定字体、字体大小、线条粗细下所占用的像素宽度和高度。
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'''
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# t_size = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
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t_size = (config[1], config[2])
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# if socre_location=='leftTop':
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p1, p2 = (pts_cls[1][0], pts_cls[0][1]), (pts_cls[1][0] + t_size[0], pts_cls[1][1])
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'''
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1. img：要绘制矩形的图像
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2. pt1：矩形框的左上角坐标，可以是一个包含两个整数的元组或列表，例如(x1, y1)或[x1, y1]。
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3. pt2：矩形框的右下角坐标，可以是一个包含两个整数的元组或列表，例如(x2, y2)或[x2, y2]。
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4. color：矩形框的颜色，可以是一个包含三个整数的元组或列表，例如(255, 0, 0)表示蓝色，或一个标量值，例如255表示白色。颜色顺序为BGR。
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5. thickness：线条的粗细，以像素为单位。如果为负值，则表示要绘制填充矩形。默认值为1。
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6. lineType：线条的类型，可以是cv2.LINE_AA表示抗锯齿线条，或cv2.LINE_4表示4连通线条，或cv2.LINE_8表示8连通线条。默认值为cv2.LINE_8。
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7. shift：坐标点小数点位数。默认值为0。
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'''
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cv2.rectangle(img, p1, p2, color, -1, cv2.LINE_AA)
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p3 = pts_cls[1][0], pts_cls[1][1] - (lh - t_size[1]) // 2
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'''
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1. img：要在其上绘制文本的图像
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2. text：要绘制的文本内容，类型为字符串
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3. org：文本起始位置的坐标，可以是一个包含两个整数的元组或列表，例如(x, y)或[x, y]。
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4. fontFace：字体类型，例如cv2.FONT_HERSHEY_SIMPLEX等。
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5. fontScale：字体大小的缩放因子，例如1.2表示字体大小增加20%。
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6. color：文本的颜色，可以是一个包含三个整数的元组或列表，例如(255, 0, 0)表示蓝色，或一个标量值，例如255表示白色。颜色顺序为BGR。
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7. thickness：文本线条的粗细，以像素为单位。默认值为1。
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8. lineType：线条的类型，可以是cv2.LINE_AA表示抗锯齿线条，或cv2.LINE_4表示4连通线条，或cv2.LINE_8表示8连通线条。默认值为cv2.LINE_8。
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9. bottomLeftOrigin：文本起始位置是否为左下角。如果为True，则文本起始位置为左下角，否则为左上角。默认值为False。
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'''
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if isNew:
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cv2.putText(img, label, p3, 0, config[3], [0, 0, 0], thickness=config[4], lineType=cv2.LINE_AA)
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else:
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cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
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return img, box
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# 动态标签
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def draw_name_joint(box, img, label_array_dict, score=0.5, color=None, config=None, name=""):
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label_array = None
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for zh in name:
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if zh in label_array_dict:
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if label_array is None:
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label_array = label_array_dict[zh]
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else:
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label_array = np.concatenate((label_array,label_array_dict[zh]), axis= 1)
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# 识别问题描述图片的高、宽
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if label_array is None:
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lh, lw = 0, 0
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else:
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lh, lw = label_array.shape[0:2]
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# 图片的长度和宽度
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imh, imw = img.shape[0:2]
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box = xywh2xyxy(box)
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# 框框左上的位置
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x0, y1 = box[0][0], box[0][1]
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x1, y0 = x0 + lw, y1 - lh
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# 如果y0小于0, 说明超过上边框
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if y0 < 0:
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y0 = 0
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# y1等于文字高度
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y1 = y0 + lh
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# 如果y1框框的高大于图片高度
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if y1 > imh:
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# y1等于图片高度
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y1 = imh
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# y0等于y1减去文字高度
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y0 = y1 - lh
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# 如果x0小于0
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if x0 < 0:
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x0 = 0
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x1 = x0 + lw
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if x1 > imw:
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x1 = imw
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x0 = x1 - lw
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tl = config[0]
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box1 = np.asarray(box, np.int32)
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cv2.polylines(img, [box1], True, color, tl)
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if label_array is not None:
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img[y0:y1, x0:x1, :] = label_array
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pts_cls = [(x0, y0), (x1, y1)]
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# 把英文字符score画到类别旁边
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# tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
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label = ' %.2f' % score
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t_size = (config[1], config[2])
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# if socre_location=='leftTop':
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p1, p2 = (pts_cls[1][0], pts_cls[0][1]), (pts_cls[1][0] + t_size[0], pts_cls[1][1])
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cv2.rectangle(img, p1, p2, color, -1, cv2.LINE_AA)
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p3 = pts_cls[1][0], pts_cls[1][1] - (lh - t_size[1]) // 2
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cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
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return img, box
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def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
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font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
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# (color=None, label=None, font=None, fontSize=40, unify=False)
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label_zh = get_label_array(color, box[0], font, outfontsize)
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return plot_one_box_auto(box[1], img, color, line_thickness, label_zh)
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def filterBox(det0, det1, pix_dis):
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# det0为 (m1, 11) 矩阵
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# det1为 (m2, 12) 矩阵
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if len(det0.shape) == 1:
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det0 = det0[np.newaxis,...]
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if len(det1.shape) == 1:
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det1 = det1[np.newaxis,...]
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det1 = det1[...,0:11].copy()
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m, n = det0.size, det1.size
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if not m:
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return det0
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# 在det0的列方向加一个元素flag代表该目标框中心点是否在之前目标框内(0代表不在，其他代表在)
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flag = np.zeros([len(det0), 1])
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det0 = np.concatenate([det0, flag], axis=1)
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det0_copy = det0.copy()
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# det1_copy = det1.copy()
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if not n:
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return det0
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# det0转成 (m1, m2, 12) 的矩阵
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# det1转成 (m1, m2, 12) 的矩阵
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# det0与det1在第3维方向上拼接(6 + 7 = 13)
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det0 = det0[:, np.newaxis, :].repeat(det1.shape[0], 1)
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det1 = det1[np.newaxis, ...].repeat(det0.shape[0], 0)
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joint_det = np.concatenate((det1, det0), axis=2)
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# 分别求det0和det1的x1, y1, x2, y2(水平框的左上右下角点)
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x1, y1, x2, y2 = joint_det[..., 0], joint_det[..., 1], joint_det[..., 4], joint_det[..., 5]
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x3, y3, x4, y4 = joint_det[..., 11], joint_det[..., 12], joint_det[..., 15], joint_det[..., 16]
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x2_c, y2_c = (x1+x2)//2, (y1+y2)//2
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x_c, y_c = (x3+x4)//2, (y3+y4)//2
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dis = (x2_c - x_c)**2 + (y2_c - y_c)**2
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mask = (joint_det[..., 9] == joint_det[..., 20]) & (dis <= pix_dis**2)
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# 类别相同 & 中心点在上一帧的框内 判断为True
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res = np.sum(mask, axis=1)
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det0_copy[..., -1] = res
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return det0_copy
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def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
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# print("省略 :%s, box:%s"%('+++' * 10, box))
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# 识别问题描述图片的高、宽
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lh, lw = label_array.shape[0:2]
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# print("省略 :%s, lh:%s, lw:%s"%('+++' * 10, lh, lw))
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# 图片的长度和宽度
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imh, imw = img.shape[0:2]
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points = None
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box = xy2xyxy(box)
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# 框框左上的位置
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x0, y1 = box[0][0], box[0][1]
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# print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
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x1, y0 = x0 + lw, y1 - lh
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# 如果y0小于0, 说明超过上边框
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if y0 < 0:
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y0 = 0
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# y1等于文字高度
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y1 = y0 + lh
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# 如果y1框框的高大于图片高度
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if y1 > imh:
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# y1等于图片高度
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y1 = imh
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# y0等于y1减去文字高度
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y0 = y1 - lh
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# 如果x0小于0
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if x0 < 0:
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x0 = 0
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x1 = x0 + lw
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if x1 > imw:
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x1 = imw
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x0 = x1 - lw
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# box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
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'''
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1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
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2. box（array）：为所画多边形的顶点坐标
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3. 所画四边形是否闭合，通常为True
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4. color（tuple）：BGR三个通道的值
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5. thickness（int）：画线的粗细
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6. shift：顶点坐标中小数的位数
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'''
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
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box1 = np.asarray(box, np.int32)
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cv2.polylines(img, [box1], True, color, tl)
|
||
img[y0:y1, x0:x1, :] = label_array
|
||
|
||
return img, box
|
||
|
||
def draw_name_crowd(dets, img, color, outfontsize=20):
|
||
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
|
||
if len(dets) == 2:
|
||
label = '当前人数：%d'%len(dets[0])
|
||
detP = dets[0]
|
||
line = dets[1]
|
||
for p in detP:
|
||
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
|
||
label_arr = get_label_array(color, label, font, outfontsize)
|
||
lh, lw = label_arr.shape[0:2]
|
||
img[0:lh, 0:lw, :] = label_arr
|
||
elif len(dets) == 3:
|
||
detP = dets[1]
|
||
line = dets[2]
|
||
for p in detP:
|
||
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
|
||
|
||
detM = dets[0]
|
||
h, w = img.shape[:2]
|
||
for b in detM:
|
||
label = '该建筑下行人及数量：%d'%(int(b[4]))
|
||
label_arr = get_label_array(color, label, font, outfontsize)
|
||
lh, lw = label_arr.shape[0:2]
|
||
# 框框左上的位置
|
||
x0, y1 = int(b[0]), int(b[1])
|
||
# print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
|
||
x1, y0 = x0 + lw, y1 - lh
|
||
# 如果y0小于0, 说明超过上边框
|
||
if y0 < 0:
|
||
y0 = 0
|
||
# y1等于文字高度
|
||
y1 = y0 + lh
|
||
# 如果y1框框的高大于图片高度
|
||
if y1 > h:
|
||
# y1等于图片高度
|
||
y1 = h
|
||
# y0等于y1减去文字高度
|
||
y0 = y1 - lh
|
||
# 如果x0小于0
|
||
if x0 < 0:
|
||
x0 = 0
|
||
x1 = x0 + lw
|
||
if x1 > w:
|
||
x1 = w
|
||
x0 = x1 - lw
|
||
|
||
cv2.polylines(img, [np.asarray(xy2xyxy(b), np.int32)], True, (0, 128, 255), 2)
|
||
img[y0:y1, x0:x1, :] = label_arr
|
||
|
||
|
||
return img, dets
|
||
|
||
def draw_name_points(img,box,color):
|
||
font = ImageFont.truetype(FONT_PATH, 6, encoding='utf-8')
|
||
points = box[-1]
|
||
arrea = cv2.contourArea(points)
|
||
label = '火焰'
|
||
arealabel = '面积:%s' % f"{arrea:.1e}"
|
||
label_array_area = get_label_array(color, arealabel, font, 10)
|
||
label_array = get_label_array(color, label, font, 10)
|
||
lh_area, lw_area = label_array_area.shape[0:2]
|
||
box = box[:4]
|
||
# 框框左上的位置
|
||
x0, y1 = box[0][0], max(box[0][1] - lh_area - 3, 0)
|
||
x1, y0 = box[1][0], box[1][1]
|
||
x0_label, y0_label, x1_label, y1_label = get_label_left(x0, y1, label_array, img)
|
||
x0_area, y0_area, x1_area, y1_area = get_label_right(x1, y0, label_array_area)
|
||
img[y0_label:y1_label, x0_label:x1_label, :] = label_array
|
||
img[y0_area:y1_area, x0_area:x1_area, :] = label_array_area
|
||
# cv2.drawContours(img, points, -1, color, tl)
|
||
cv2.polylines(img, [points], False, color, 2)
|
||
if lw_area < box[1][0] - box[0][0]:
|
||
box = [(x0, y1), (x1, y1), (x1, box[2][1]), (x0, box[2][1])]
|
||
else:
|
||
box = [(x0_label, y1), (x1, y1), (x1, box[2][1]), (x0_label, box[2][1])]
|
||
box = np.asarray(box, np.int32)
|
||
cv2.polylines(img, [box], True, color, 2)
|
||
return img, box
|
||
|
||
def draw_name_border(box,color,label_array,border):
|
||
box = xywh2xyxy(box[:4])
|
||
cx, cy = int((box[0][0] + box[2][0]) / 2), int((box[0][1] + box[2][1]) / 2)
|
||
flag = cv2.pointPolygonTest(border, (int(cx), int(cy)),
|
||
False) # 若为False，会找点是否在内，外，或轮廓上
|
||
if flag == 1:
|
||
color = [0, 0, 255]
|
||
# 纯白色是(255, 255, 255)，根据容差定义白色范围
|
||
lower_white = np.array([255 - 30] * 3, dtype=np.uint8)
|
||
upper_white = np.array([255, 255, 255], dtype=np.uint8)
|
||
# 创建白色区域的掩码（白色区域为True，非白色为False）
|
||
white_mask = cv2.inRange(label_array, lower_white, upper_white)
|
||
# 创建与原图相同大小的目标颜色图像
|
||
target_img = np.full_like(label_array, color, dtype=np.uint8)
|
||
# 先将非白色区域设为目标颜色，再将白色区域覆盖回原图颜色
|
||
label_array = np.where(white_mask[..., None], label_array, target_img)
|
||
return color,label_array
|
||
|
||
def draw_transparent_red_polygon(img, points, alpha=0.5):
|
||
"""
|
||
在图像中指定的多边形区域绘制半透明红色
|
||
|
||
参数:
|
||
image_path: 原始图像路径
|
||
points: 多边形顶点坐标列表，格式为[(x1,y1), (x2,y2), ..., (xn,yn)]
|
||
output_path: 输出图像路径
|
||
alpha: 透明度系数，0-1之间，值越小透明度越高
|
||
"""
|
||
# 读取原始图像
|
||
if img is None:
|
||
raise ValueError(f"无法读取图像")
|
||
|
||
# 创建与原图大小相同的透明图层（RGBA格式）
|
||
overlay = np.zeros((img.shape[0], img.shape[1], 4), dtype=np.uint8)
|
||
|
||
# 将点列表转换为适合cv2.fillPoly的格式
|
||
#pts = np.array(points, np.int32)
|
||
pts = points.reshape((-1, 1, 2))
|
||
|
||
# 在透明图层上绘制红色多边形（BGR为0,0,255）
|
||
# 最后一个通道是Alpha值，控制透明度，黄色rgb
|
||
cv2.fillPoly(overlay, [pts], (255, 0, 0, int(alpha * 255)))
|
||
|
||
# 将透明图层转换为BGR格式（用于与原图混合）
|
||
overlay_bgr = cv2.cvtColor(overlay, cv2.COLOR_RGBA2BGR)
|
||
|
||
# 创建掩码，用于提取红色区域
|
||
mask = overlay[:, :, 3] / 255.0
|
||
mask = np.stack([mask] * 3, axis=-1) # 转换为3通道
|
||
|
||
# 混合原图和透明红色区域
|
||
img = img * (1 - mask) + overlay_bgr * mask
|
||
img = img.astype(np.uint8)
|
||
|
||
# # 保存结果
|
||
# cv2.imwrite(output_path, result)
|
||
|
||
return img |