Python pyplot.Polygon方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def plot_box(box, box_format='xywh', color='r', linewidth=1, normalized=False, vertices=False):
    if box_format == 'xywh': # opencv
        xmin, ymin, w, h = box
        xmax, ymax = xmin + w, ymin + h
    elif box_format == 'xyxy':
        xmin, ymin, xmax, ymax = box
    if box_format == 'polygon':
        xy_rec = np.reshape(box, (-1, 2))
    else:
        xy_rec = np.array([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]])
    if normalized:
        im = plt.gci()
        xy_rec = xy_rec * np.tile(im.get_size(), (4,1))
    ax = plt.gca()
    ax.add_patch(plt.Polygon(xy_rec, fill=False, edgecolor=color, linewidth=linewidth))
    if vertices:
        c = 'rgby'
        for i in range(4):
            plt.plot(xy_rec[i,0],xy_rec[i,1], c[i], marker='o', markersize=4) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def drawComplex(data, ph, axes=[-6, 8, -6, 6]):
    plt.clf()
    plt.axis(axes)  # axes = [x1, x2, y1, y2]
    plt.scatter(data[:, 0], data[:, 1])  # plotting just for clarity
    for i, txt in enumerate(data):
        plt.annotate(i, (data[i][0] + 0.05, data[i][1]))  # add labels

    # add lines for edges
    for edge in [e for e in ph.ripsComplex if len(e) == 2]:
        # print(edge)
        pt1, pt2 = [data[pt] for pt in [n for n in edge]]
        # plt.gca().add_line(plt.Line2D(pt1,pt2))
        line = plt.Polygon([pt1, pt2], closed=None, fill=None, edgecolor='r')
        plt.gca().add_line(line)

    # add triangles
    for triangle in [t for t in ph.ripsComplex if len(t) == 3]:
        pt1, pt2, pt3 = [data[pt] for pt in [n for n in triangle]]
        line = plt.Polygon([pt1, pt2, pt3], closed=False,
                           color="blue", alpha=0.3, fill=True, edgecolor=None)
        plt.gca().add_line(line)
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def drawComplex(origData, ripsComplex, axes=[-6,8,-6,6]):
  plt.clf()
  plt.axis(axes)
  plt.scatter(origData[:,0],origData[:,1]) #plotting just for clarity
  for i, txt in enumerate(origData):
      plt.annotate(i, (origData[i][0]+0.05, origData[i][1])) #add labels

  #add lines for edges
  for edge in [e for e in ripsComplex if len(e)==2]:
      #print(edge)
      pt1,pt2 = [origData[pt] for pt in [n for n in edge]]
      #plt.gca().add_line(plt.Line2D(pt1,pt2))
      line = plt.Polygon([pt1,pt2], closed=None, fill=None, edgecolor='r')
      plt.gca().add_line(line)

  #add triangles
  for triangle in [t for t in ripsComplex if len(t)==3]:
      pt1,pt2,pt3 = [origData[pt] for pt in [n for n in triangle]]
      line = plt.Polygon([pt1,pt2,pt3], closed=False, color="blue",alpha=0.3, fill=True, edgecolor=None)
      plt.gca().add_line(line)
  plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def plotTZ(filename=None):
    t = np.linspace(0, 1, 101)
    z = 0.25 + 0.5 / (1 + np.exp(- 20 * (t - 0.5))) + 0.05 * np.cos(t * 2 * np.pi)
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    poly1 = [[0, 0]]
    poly1.extend([[t[i], z[i]] for i in range(t.size)])
    poly1.extend([[1, 0], [0, 0]])
    poly2 = [[0, 1]]
    poly2.extend([[t[i], z[i]] for i in range(t.size)])
    poly2.extend([[1, 1], [0, 1]])
    poly1 = plt.Polygon(poly1,fc=cmap(0.0))
    poly2 = plt.Polygon(poly2,fc=cmap(1.0))
    ax1.add_patch(poly1)
    ax1.add_patch(poly2)
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('True Data', size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Output y', size=22)
    plt.show()
    if not filename is None:
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def plot_assignement(self, map_idx):
        ax = plt.gca()
        im = plt.gci()
        img_h, img_w = image_size = im.get_size()
        
        # ground truth
        boxes = self.gt_boxes
        boxes_x = (boxes[:,0] + boxes[:,2]) / 2. * img_h
        boxes_y = (boxes[:,1] + boxes[:,3]) / 2. * img_w
        for box in boxes:
            xy_rec = to_rec(box[:4], image_size)
            ax.add_patch(plt.Polygon(xy_rec, fill=False, edgecolor='b', linewidth=2))
        plt.plot(boxes_x, boxes_y, 'bo',  markersize=6)
        
        # prior boxes
        for idx, box_idx in self.match_indices.items():
            if idx >= self.map_offsets[map_idx] and idx < self.map_offsets[map_idx+1]:
                x, y = self.priors_xy[idx]
                w, h = self.priors_wh[idx]
                plt.plot(x, y, 'ro',  markersize=4)
                plt.plot([x, boxes_x[box_idx]], [y, boxes_y[box_idx]], '-r', linewidth=1)
                ax.add_patch(plt.Rectangle((x-w/2, y-h/2), w+1, h+1, 
                        fill=False, edgecolor='y', linewidth=2)) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def polygon_iou(list1, list2):
    """
    Intersection over union between two shapely polygons.
    """
    polygon_points1 = np.array(list1).reshape(4, 2)
    poly1 = Polygon(polygon_points1).convex_hull
    polygon_points2 = np.array(list2).reshape(4, 2)
    poly2 = Polygon(polygon_points2).convex_hull
    union_poly = np.concatenate((polygon_points1, polygon_points2))
    if not poly1.intersects(poly2):  # this test is fast and can accelerate calculation
        iou = 0
    else:
        try:
            inter_area = poly1.intersection(poly2).area
            # union_area = poly1.area + poly2.area - inter_area
            union_area = MultiPoint(union_poly).convex_hull.area
            if union_area == 0:
                return 0
            iou = float(inter_area) / union_area
        except shapely.geos.TopologicalError:
            print('shapely.geos.TopologicalError occured, iou set to 0')
            iou = 0
    return iou 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def plot_result(
        self,
        axis_values,
        force_1=None,
        force_2=None,
        digits=2,
        node_results=True,
        fill_polygon=True,
        color=0
    ):
        if fill_polygon:
            rec = plt.Polygon(
                np.vstack(axis_values).T, color="C{}".format(color), alpha=0.3
            )
            self.one_fig.add_patch(rec)
        # plot force
        x_val = axis_values[0]
        y_val = axis_values[1]

        self.one_fig.plot(x_val, y_val, color="C{}".format(color))

        if node_results:
            self._add_node_values(x_val, y_val, force_1, force_2, digits) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def mass_center(self):
        '''mass center of a node'''
        shape = self.brush.style[1]
        if isinstance(self.obj, (plt.Polygon, patches.PathPatch)):
            pos = self._clean_path.mean(axis=0)
        else:
            pos = self.position
        return Pin(pos) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def height(self):
        shape = self.brush.style[1]
        if isinstance(self.obj, plt.Circle):
            return self.obj.radius * 2
        elif isinstance(self.obj, plt.Rectangle):
            return self.obj.get_height()
        elif isinstance(self.obj, patches.FancyBboxPatch):
            return self.obj.get_height() + 2*self.obj.get_boxstyle().pad
        elif isinstance(self.obj, (plt.Polygon, patches.PathPatch)):
            y = self.path[:,1]
            return y.max() - y.min()
        else:
            raise 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def width(self):
        shape = self.brush.style[1]
        if isinstance(self.obj, plt.Circle):
            return self.obj.radius * 2
        elif isinstance(self.obj, plt.Rectangle):
            return self.obj.get_width()
        elif isinstance(self.obj, patches.FancyBboxPatch):
            return self.obj.get_width() + 2*self.obj.get_boxstyle().pad
        elif isinstance(self.obj, (plt.Polygon, patches.PathPatch)):
            x = self.path[:,0]
            return x.max() - x.min()
        else:
            raise 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def plot_results(self, results=None, classes=None, show_labels=True, gt_data=None, confidence_threshold=None):
        if results is None:
            results = self.results
        if confidence_threshold is not None:
            mask = results[:, 4] > confidence_threshold
            results = results[mask]
        if classes is not None:
            colors = plt.cm.hsv(np.linspace(0, 1, len(classes)+1)).tolist()
        ax = plt.gca()
        im = plt.gci()
        image_size = im.get_size()
        
        # draw ground truth
        if gt_data is not None:
            for box in gt_data:
                label = np.nonzero(box[4:])[0][0]+1
                color = 'g' if classes == None else colors[label]
                xy_rec = to_rec(box[:4], image_size)
                ax.add_patch(plt.Polygon(xy_rec, fill=True, color=color, linewidth=1, alpha=0.3))
        
        # draw prediction
        for r in results:
            label = int(r[5])
            confidence = r[4]
            color = 'r' if classes == None else colors[label]
            xy_rec = to_rec(r[:4], image_size)
            ax.add_patch(plt.Polygon(xy_rec, fill=False, edgecolor=color, linewidth=2))
            if show_labels:
                label_name = label if classes == None else classes[label]
                xmin, ymin = xy_rec[0]
                display_txt = '%0.2f, %s' % (confidence, label_name)        
                ax.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5}) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def plot_gt(self, boxes, show_labels=True):
        # if parameter is sample index
        if type(boxes) in [int]:
            boxes = self.data[boxes]
        
        ax = plt.gca()
        im = plt.gci()
        w, h = im.get_size()
        
        for box in boxes:
            class_idx = int(box[-1])
            color = self.colors[class_idx]
            is_polygon = len(box)-1 > 4
            if is_polygon:
                xy = box[:-1].reshape((-1,2))
            else:
                xmin, ymin, xmax, ymax = box[:4]
                xy = np.array([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]])
            xy = xy * [h, w]
            ax.add_patch(plt.Polygon(xy, fill=False, edgecolor=color, linewidth=1))
            if show_labels:
                label_name = self.classes[class_idx]
                if is_polygon:
                    angle = np.arctan((xy[1,0]-xy[0,0])/(xy[1,1]-xy[0,1]+eps))
                    if angle < 0:
                        angle += np.pi
                    angle = angle/np.pi*180-90
                else:
                    angle = 0                
                ax.text(xy[0,0], xy[0,1], label_name, bbox={'facecolor':color, 'alpha':0.5}, rotation=angle) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def polygon_from_list(line):
    """
    Create a shapely polygon object from gt or dt line.
    """
    # polygon_points = [float(o) for o in line.split(',')[:8]]
    polygon_points = np.array(line).reshape(4, 2)
    polygon = Polygon(polygon_points).convex_hull
    return polygon 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def visulization(img_dir, bbox_dir, visu_dir):
    for root, dirs, files in os.walk(img_dir):
        for file in files:
            # print(file)
            # if file!='img_75.jpg':
            #   continue
            print(file)
            image_name = file
            img_path = os.path.join(img_dir, image_name)
            img = cv2.imread(img_path)
            plt.clf()
            plt.imshow(img)
            currentAxis = plt.gca()
            bbox_name = 'res_' + file[0:len(file) - 3] + 'txt'
            bbox_path = os.path.join(bbox_dir, bbox_name)
            if os.path.isfile(bbox_path):
                with open(bbox_path, 'r') as f:
                    count = 1
                    for line in f.readlines():
                        line = line.strip()
                        x1 = line.split(',')[0]
                        y1 = line.split(',')[1]
                        x2 = line.split(',')[2]
                        y2 = line.split(',')[3]
                        x3 = line.split(',')[4]
                        y3 = line.split(',')[5]
                        x4 = line.split(',')[6]
                        y4 = line.split(',')[7]
                        rbox = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])
                        color_rbox = 'r'
                        currentAxis.add_patch(plt.Polygon(rbox, fill=False, edgecolor=color_rbox, linewidth=1))
                        # currentAxis.text(int(x1), int(y1), str(count), bbox={'facecolor':'white', 'alpha':0.5})
                        count = count + 1

                plt.axis('off')
                plt.savefig(visu_dir + image_name, dpi=300) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import Polygon [as 別名]
def __roll_support_patch(self, max_val):
        """
        :param max_val: max scale of the plot
        """
        radius = PATCH_SIZE * max_val
        count = 0
        for node in self.system.supports_roll:
            direction = self.system.supports_roll_direction[count]
            x1 = np.cos(np.pi) * radius + node.vertex.x + radius
            z1 = np.sin(np.pi) * radius + node.vertex.y
            x2 = np.cos(np.radians(90)) * radius + node.vertex.x + radius
            z2 = np.sin(np.radians(90)) * radius + node.vertex.y
            x3 = np.cos(np.radians(270)) * radius + node.vertex.x + radius
            z3 = np.sin(np.radians(270)) * radius + node.vertex.y

            triangle = np.array([[x1, z1], [x2, z2], [x3, z3]])

            if node.id in self.system.inclined_roll:
                angle = self.system.inclined_roll[node.id]
                triangle = rotate_xy(triangle, angle + np.pi * 0.5)
                support_patch = plt.Polygon(triangle, color="r", zorder=9)
                self.one_fig.add_patch(support_patch)
                self.one_fig.plot(
                    triangle[1:, 0] - 0.5 * radius * np.sin(angle),
                    triangle[1:, 1] - 0.5 * radius * np.cos(angle),
                    color="r",
                )

            elif direction == 2:  # horizontal roll
                support_patch = mpatches.RegularPolygon(
                    (node.vertex.x, node.vertex.y - radius),
                    numVertices=3,
                    radius=radius,
                    color="r",
                    zorder=9,
                )
                self.one_fig.add_patch(support_patch)
                y = -node.vertex.z - 2 * radius
                self.one_fig.plot(
                    [node.vertex.x - radius, node.vertex.x + radius], [y, y], color="r"
                )
            elif direction == 1:  # vertical roll
                # translate the support to the node

                support_patch = mpatches.Polygon(triangle, color="r", zorder=9)
                self.one_fig.add_patch(support_patch)

                y = node.vertex.y - radius
                self.one_fig.plot(
                    [node.vertex.x + radius * 1.5, node.vertex.x + radius * 1.5],
                    [y, y + 2 * radius],
                    color="r",
                )
            count += 1